Hive Digital Technologies CEO Aydin Kilic joined Steve Darling from Proactive to announce the company&#x27;s earnings report for the third quarter.Kilic revealed that the company achieved remarkable...

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However you work, hive can help. Align on project goals, timeline, scope and objectives in one central location. Plan and track goals across your company in Hive.

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Look up hive, hivé, or hives in Wiktionary, the free dictionary. A hive may refer to a beehive, an enclosed structure in which some honey bee species live and raise their young. ... H.I.V.E. (&quot;Hierarchy of International Vengeance and Extermination&quot;), a DC Comics villain organization · H.I.V.E. ...

Hive (company)

Hive is an American artificial intelligence company offering machine learning models via APIs to enterprise customers. Hive uses around 700,000 gig workers to train data for its models through its Hive Work app. One of Hive&#x27;s major offerings is to provide automated content moderation services.

Hive (artificial intelligence company) - Wikipedia

Hive is an AI company focused on building next-generation intelligent automation solutions. Today, some of the largest companies in the world are leveraging Hive’s best-in-class models to process billions of pieces of data each month, solving challenges in content moderation, ... Read more

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AI Infrastructure for the Web3 Digital Transformation.

HIVE Digital Technologies Ltd

We power innovators to solve problems with practical AI solutions and data labeling, grounded in the world’s highest quality visual &amp; audio metadata

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They ended up creating an amazing company that also does amazing things for the earth. They are proudly a zero waste company and partner with nonprofit organizations.Shop The Real Dill ... See how every product at Hive has a tangible, positive impact on the planet, from production to packaging. ...

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The Hive 5 process coaches students through five steps to creating a strong, informative, representative resume. Employer companies benefit from students being prepared and ready for interviews. Learn more about our app experience →

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Uquid, a blockchain e-commerce ecosystem, has continued to make significant gains towards fostering adoption in the global market. It has developed an innovative platform that incorporates DeFi, NFTs and metaverse. It has embarked on strategic partnerships with top blockchain platforms to expand its ecosystem further. Its latest collaboration is with Hive – an innovative decentralized blockchain and ecosystem. A significant partnership will collaborate with Hive on different blockchain projects, including collaborations in Web 3 development. In addition, Hive will further assist Uquid with valuable information to help the blockchain protocol optimize the user’s shopping experience. Uquid Uquid will provide Hive with an enabling environment to bring its innovative technology closer to real-life applications via its e-commerce solution and existing community of users. The partnership will also offer Uquid access to develop its product to fit Web 3.0. is regarded as one of the top blockchain platforms and provides a decentralized information-sharing network. It supports different dApps that enable content producers, advertisers, and readers to store and monetize content. Hive It is also designed to scale with fast processing times and fee-less transactions, making it ideal for projects to build dApps. As expected, the news of the partnership has created excitement within the crypto community, and there are huge expectations that it would make an innovative experience for Uquid members. It will also create a secure and faster shopping experience for users and more features on the Uquid marketplace. A fast-growing ecosystem is regarded as one of the fastest-growing shopping ecosystems in the blockchain industry. Since its launch in 2016, Uquid has continued developing and diversifying its array of products to service millions of users globally. Uquid Unlike other existing e-commerce blockchain platforms, Uquid has developed an extensive infrastructure that includes DeFi, Metaverse and NFTs. This attention to emerging trends in the blockchain sector has led to massive adoption by merchants and shoppers. Uquid has reached more than 40 million products in its ecosystem and integrated DeFi features that enable loyal customers to earn from shopping. The blockchain ecosystem has already stated its ambition to be the e-commerce domain of web 3.0 and incorporate up to 120 million products by 2023. Its latest product launched in 2021, “Payment in 3”, was a major success and enabled users to pay installments for products using crypto. This has led to easier crypto adoption within the e-commerce scene. This latest partnership with Hive is great and should provide a significant push towards Uquid adoption globally.

About Our Strategic Partnership with Hive

Are you using SQL engines, such as Presto, to query existing Hive data warehouse and are experiencing challenges including overloaded Hive Metastore with slow and unpredictable access, unoptimized data formats and layouts such as too many small files, or lack of influence over the existing Hive system and other Hive applications? To address these challenges, we launched in the 2.1 open source release, which includes the . Alluxio Structured Data Management Alluxio Catalog Service Here is the FAQ based on the top questions from the community: How Does the Alluxio Catalog Service Work With Hive Metastore? The manages metadata for structured data, such as schema and table information. To do so, The Alluxio catalog manages connections to external metadata services (like Hive Metastore or AWS Glue) via an abstraction called Under Database (UDB). With the UDB abstraction, the Alluxio catalog can interact with other external metadata services for database and table information. Alluxio catalog For the Alluxio catalog to manage and maintain interactions with external metadata services, the external service must be “attached” to the Alluxio catalog. This attaching process adds the relationship between the external service and the Alluxio catalog. Attaching an external metadata service is initiated via the . “attachdb” CLI command In the Alluxio 2.2 release, there is an implementation of the UDB for the Hive Metastore. This means databases in a Hive Metastore can be attached to the Alluxio catalog so that the Alluxio catalog can manage and serve this metadata. In future versions, support for the AWS Glue UDB will be added. After an external database is attached, the Alluxio catalog syncs all the information from the external database and imports the table definitions into the catalog. The Alluxio catalog can then serve the information to the client, which in this case is in the Alluxio connector in Presto. The triggers the Alluxio catalog to sync with the UDB again. “sync” CLI command Once a UDB is attached, the Alluxio catalog manages the table metadata. This means the Alluxio catalog will transparently use Alluxio file locations instead of the locations found in the Hive Metastore without modifying the Hive Metastore. This allows simple deployment of the Alluxio catalog service with existing Hive Metastores. How Does the Alluxio Catalog Service Work With Presto? Presto interacts with the Alluxio catalog via the Alluxio connector. The Alluxio connector in Presto is new functionality for the existing Hive connector. The new functionality has already been merged into the codebases of PrestoSQL and PrestoDB. The Presto catalog needs to be instead of the Hive Metastore. Once Presto is configured in this way, all of the existing Hive connector operations will interact with the Alluxio catalog for the metadata of tables and not the Hive Metastore. configured to interact with the Alluxio catalog In the Alluxio 2.2 release, only read-only queries are supported with the Alluxio connector and Alluxio catalog. In future versions, write queries like DDL and DML statements will be supported. How do Alluxio Transformations Work? are performed by an , which executes distributed, asynchronous tasks. When a transformation is initiated, this is converted into a distributed job and executed across the various Alluxio job workers. Once the job is complete, the Alluxio catalog is updated with the newest transformed locations. When the Alluxio catalog serves the table metadata, the new transformed locations are used transparently, without any modification to the UDB. Alluxio transformations Alluxio-internal job service When transformations are generated, they are written through to the underlying persistent store and not cached in Alluxio. Once the transformed data is written, it can be read from the new transformed locations. The first read of the transformed data will cache the data in Alluxio on-demand. In Alluxio 2.2, the output file format is the parquet format since it is a more compute-optimized file format. In future versions, additional file formats and parquet output options will be available. For coalescing many small files together, the default behavior is to generate files of about 2G, but that is configurable. What is the Architecture of Alluxio Structured Data Services? Alluxio Structured Data Services is a part of the and has 3 main components: Alluxio connector in Presto, Alluxio Catalog Service, and Alluxio Transformation Service. Alluxio system The Alluxio connector is part of the Presto SQL engine. It is a connector based on the existing Hive connector and is the client to the Alluxio catalog. Alluxio connector: The catalog service manages the metadata for structured data and is a part of the Alluxio master processes. Alluxio Catalog Service: The transformation service is managed by the Alluxio catalog, and the transformations are performed by the Alluxio job service, which is an internal service for executing distributed, asynchronous tasks. The jobs are executed and managed by the Alluxio job masters and Alluxio job workers. Alluxio Transformation Service: Also Published At: https://www.alluxio.io/blog/everything-you-want-to-know-about-how-to-decouple-sql-engines-from-hive-data-warehouse/

A Primer on Decoupling SQL Engines from Hive Data Warehouse

In this article, Thai Bui describes how Bazaarvoice leverages Alluxio as a caching tier on top of  AWS S3 to maximize performance and minimize operating costs on running Big Data analytics on AWS EC2. The original article can be found on . Alluxio's engineering blog This article aims to provide the following takeaways: Common challenges in building an efficient big data analytics platform on AWS How to setup Hive metastore to leverage Alluxio as the storage tier for frequently accessed tables, backed by all tables on AWS S3 as the source of truth How to setup tiered storage within Alluxio based on ZFS and NVMe on EC2 instances to maximize the read performance Benchmark results of micro and real-world workloads Our Big Data Workload and Platform on AWS is a digital marketing company based in Austin, Texas. We are a software-as-a-service provider that allows retailers and brands to curate, manage, and understand user-generated content (UGC) such as reviews for their products. We provide services such as  collecting and hosting reviews, programmatic management of UGC content, and deep analytics on consumer behavior. Bazaarvoice Our globally distributed services analyze UGC content for over 1900 of the biggest internet retailers and brands. Data engineering at Bazaarvoice requires handling data at massive internet-scale.  Take , for example, the combined 3-day holiday traffic generated 1.5 billion product page views and recorded $5.1 billion USD in online sales for our clients. Within a month, our services host and record web analytics events for over 900 million internet shoppers worldwide. 2018 Thanksgiving weekend To keep up with the web traffic, we host our services via Amazon Web Service. The big data platform completely relies on the open source Hadoop ecosystem, utilizing tools such as: Apache Hive, Spark for ETLs Kafka, Storm for unbounded dataset processing Cassandra, ElasticSearch and HBase for durable datastore and long-term aggregations The data generated by these various services is optimally formatted for analytics, such as Parquet or ORC, and is eventually stored in S3. Why Alluxio Using S3 to store our data enables us to increase our storage capacity effortlessly, but at the cost of incurring a performance bottleneck. Accessing sizable data from S3 is bottlenecked by the connection’s bandwidth. Since S3 is a managed service by AWS, it is not possible to write custom code to optimize to our specific environment, contrary to other open source technologies being utilized. One approach would be to provision more hardware to process data from S3 in parallel. But rather than spending more money on hardware, we decided to find a more ideal solution to solve this performance bottleneck. We realized that not all data access needs to be fast because the workloads typically involved a subset of data. However this selective subset is constantly changing as newer data is added. This led us to come up with a tiered storage system to accelerate our data access, with the following goals: It should be nimble by expanding as our business grows without any data movement or introducing ETL jobs It should provide the same level of interoperability for most of our existing systems It should allow us to scale by utilizing better hardware when our budget allows for it. With these criteria in mind, we decided to use to accelerate our data access. As a key component in the tiered storage system, Alluxio was picked as it is highly configurable and relatively cheap to reconfigure operational-wise. Alluxio Architecture with Alluxio We integrated Alluxio with Hive Metastore as a basis for the tiered storage S3 acceleration layer. Clusters can be elastically provisioned with support for our tiered storage layer. In each job cluster (Hive on Tez & Spark) or interactive cluster (Hive 3 on LLAP), a table could be altered to have more relevant data configured to use Alluxio, while having the rest of the data served by S3 directly. Since the data is not cached in Alluxio unless it is accessed via a Hive or Spark task, there’s no data movement. Since updating a table configuration is extremely cheap, adapting this pattern of changing queries  has allowed us to be very nimble. For example, let us look at our page view dataset. A page view is an event recorded when an Internet user visits a page in our client’s website. The raw data is collected, aggregated hourly, and converted into Parquet format. This data is stored in S3 in a year, month, day, and hour hierarchical structure: s3: |- pageview/
 |- /
 |- /
 |- /
 |- /
 |- /
 |- /
 |- pageview_file1.parquet
 |- pageview_file2.parquet //some-bucket/ 2019 02 14 01 02 03 The data is registered to one of our clusters via Hive Metastore and available to be used in Hive or Spark for analytics, ETLs, and other various purposes. An internal system registers the data and updates the Hive Metastore directly for every new dataset that it detects. # add partition from s3. equivalent to always referencing “cold” data
ALTER TABLE pageview 
 ADD LOCATION ‘s3: PARTITION (year= , month= , day= , hour= ) 2019 2 14 3 //<bucket>/pageview/2019/02/14/03’ Our system is also aware of the tiered storage configuration provided for each specific cluster and table. For instance, in an interactive cluster where analysts are analyzing the last few weeks of trending data, the table with partitions less than a month old is configured to read directly from the Alluxio filesystem. An internal system reads this configuration to mount the S3 bucket to the Alluxio cluster via REST API and automatically takes care of promoting or demoting tables and partitions using the following Hive DDLs: pageview # promoting. repeating query will cache the data from cold->warm-> hot
ALTER TABLE pageview SET LOCATION ‘alluxio: # demoting. 1 month older data goes back to the “cold” tier.
# protects our cache when people occasionally read older data
ALTER TABLE pageview SET LOCATION ‘s3: PARTITION (year= , month= , day= , hour= ) 2019 2 14 3 //<address>/mnt/s3/pageview/2019/02/14/03’ this PARTITION (year= , month= , day= , hour= ) 2019 1 14 3 //<bucket>/pageview/2019/01/14/03’ As more recent data arrives, older data is demoted, requiring the tiered storage configuration to be updated. This process happens asynchronously and continuously. Micro and Real-world Benchmark Results Our Alluxio + ZFS + NVMe SSD read micro benchmark is run on an i3.4xlarge AWS instance with up to 10 Gbit network, 128GB of RAM, and two 1.9TB NVMe SSDs. We mount a S3 bucket on Alluxio to perform 2 read tests. The first test copies 5GB of Parquet data using the AWS CLI into the instance’s ramdisk to measure only read performance. # using AWS cli to copy recursively to RAM disk
time aws s3 cp --recursive s3: //<bucket>/dir /mnt/ramdisk The second test uses the Alluxio CLI to copy the same Parquet data to ramdisk. This time, we perform the test 3 times to get the cold, warm, and hot numbers as shown in the chart above. # using AWS cli to copy recursively to RAM disk
time ./bin/alluxio fs copyToLocal /mnt/s3/dir /mnt/ramdisk Alluxio v1.7 with ZFS and NVMe takes about 78% longer to perform a cold read when compared to S3 (66s vs. 37s). The successive warm and hot reads are 2.5 and 4.6 times faster than reading directly from S3. However microbenchmarks don’t always tell the whole story, thus we will take a look at a few real-world queries executed by actual analysts at Bazaarvoice. We rerun the same queries on our production interactive cluster to compare the tiered storage system  against S3 by itself. The production cluster consists of 20 i3.4xlarge nodes running Hadoop 3, Hive 3.0 on Alluxio 1.7, and ZFS v0.7.12-1. Query 1 is a single table deduplication query with 6 columns to group by. It processes 95M input records and 8G of Parquet data from a 200G dataset. Query 2 is a more complex query, involving a join on 4 tables with aggregations across 5 columns. It processes 1.2B input records and 50G of Parquet data from several TB datasets. The two queries simplified for readability and their query plans are shown below. Compared to running the queries directly on S3, the tiered storage system speeds up query 1 by 11x and query 2 by 6x. # query simplified
SELECT dt, col1, col2, col3, col4, = lower( )
 AND month = AND year = AND (col7 IS NOT NULL OR col8 IS NOT NULL)
GROUP BY dt, col1, col2, col3, col4, col5 
ORDER BY dt DESC
LIMIT ; 1 col5 
FROM table_1 n
WHERE lower (col6) '<some_value>' 1 2019 100 Conclusion Alluxio, ZFS, and the tiered storage architecture have helped us save a significant amount of time for analysts at Bazaarvoice. AWS S3 is easy to scale in capacity and by augmenting it with a tiered storage configuration that is nimble and cheap to adapt, we can focus on growing our business and scaling storage as needed. Today at Bazaarvoice, the current production configuration can handle about 35TB of data in cache and half a petabyte of data on S3. In the future, we could simply add more nodes to grow the cache size or upgrade hardware for quicker localized access.

Accelerate Spark and Hive Jobs on AWS S3 by 10x with Alluxio as a Tiered Storage Solution

Delegated Proof of Stake (DPOS) is highly misunderstood, and underestimated when used correctly. I believe is one of the most anti-fragile and pure DPOS blockchains ever seen. I have always said DPOS chains needed a social element. For Most DPOS chains, the chain users know very little to nothing at all about the block producers or running the chain. HIVE "witnesses" On a social blockchain such as Hive, I know every single block producer in the top 20 , maybe not by name but by reputation, work added and stories they share. This connection between witness and stakeholder adds tremendous agility, as we have seen with the Steem/Hive split. (consensus) DPOS is underestimated because we have never seen a true overtake threat that was met with resistance. Without the social connection, stakeholders lose the ability to communicate with the block producers. DPOS will generally have some of what I call sleeper voters. People who only vote when they need to, even if incentivized. On social blockchains like Hive, voter participation is much greater, because the social activity grows bonds. DPOS will have many different factions, people who agree and disagree, like and dislike each other, this is where the decentralization comes into play. In the case with Steem, Justin Sun getting exchanges to vote with user’s funds to the community elected witnesses. It takes an alien invasion like a threat, to bring all "countries" together so to speak. unwittingly (locking them for 13 weeks) overthrow As one of the largest stakeholders of STEEM, I woke up one morning shortly after Justin Sun purchased Steemit inc to see 30 witnesses at the top ranks I had never seen. All of the new witness accounts were just created, all run on the same server by the same person, Justin Sun; one serious implication and the result would be a chain halt. I wonder if they had the knowledge to restart a complicated chain like Steem if they had to deal with such a big problem. That was/is a threat, for everyone. Having invested as much as I had I felt this was a threat to my investment, a hostile takeover. In what was later described as an on the Steem network, Justin Sun, and TRON had forcibly overtaken the network consensus with the help of three of the world’s largest , Binance, Huobi & Poloenix (part-owned by Justin Sun.) This action was so completely unprecedented and unlikely, that it was not anticipated as a weakness of the Steem blockchain by the vast majority in the community. SYBIL attack crypto exchanges The back story: detailed version. The back story, The STEEM creators "ninja mined" the majority of the STEEM token supply into existence. This was done in order to avoid legal issues from being classed as a centralized chain and thus considered a security. The creators of the blockchain vowed to never vote with the ninja-mined STEEM, and that they would use it only to develop and further decentralized the ecosystem. Investors invested base on these promises and on the fact that Steemit INC., being a USA company, would have legal issues if they tried to exit scam. What instead happened was the STEEM development fund was sold to TRON, in secret, by Ned Scott. Not even the Steemit INC. team were told about this sale ahead of time, and especially not the community. Most importantly the witnesses were not in the loop either. TLDR version: (mined at an unfair advantage at the start) On STEEM/HIVE, the DPOS system requires 17 of the top 20 witnesses to hard fork the network for proposal upgrades. To date, 23 hard forks had already successfully been done on STEEM, with the 23rd Rebranding STEEM to HIVE. TRON exploited custodial user funds on exchanges to vote in 20 of their own top 20 witnesses in what was referred to as witnesses. TRON exploited users' funds to overthrow the very network the users themselves were invested in. "sock puppet" The Steem hostile takeover resulted in most of the Steemit INC. team quitting in protest, such as head of communications , and blockchain developers , and . Over 50% of the active DApps on the Steem blockchain have to Hive. Andrew Levine Michael Vandeberg Steve Gerbino Roadscape migrated No DPOS blockchain and arguably any blockchain ever have experienced such an attack. The majority stakeholder acting in a hostile manner towards their investment is the ultimate attack scenario we have only dramatized about in the blockchain industry and wider community. “money” Fooling exchanges to assist with this type of hostile takeover was truly unprecedented. Bitcoin vs Bitcoin cash was similar in a lot of ways, but DPOS having stake-based voting and STEEM being a social media blockchain with a strong dedicated community made the stakes and human element much higher. It was a fight for the chain's life. This was the alien invasion scenario. At the time of overtaking the network, the highest witness spot had 40million STEEMPOWER backing it What happened next was amazing. The sleeper voters woke up! Despite everyone's differences people rallied together to vote in the community witnesses. The vote war raged on and 15 days of battle ensued and at one point the top community witness had 90 million SP backing it (about 11% of the total possible vote.) (about 25% of the total possible vote!) We were able to reclaim 10 of the top 10 spots causing a stalemate on the hostile takeover. Considering that TRON via their purchase of Steemit INC held about 20% themselves, this was a significant phenomenon wherein a community rallied together to defend itself against a centralized, hostile foe. Some ground-level insights into what the attacker did so that others can learn from what is even possible in such an attack. Justin was losing the voting war halfway through and started offering people $2,500 each month to run a witness node for him on Steem; this is called a bribe attack. Justin also was in discord servers promising anything the users wanted to hear in exchange for votes, even things technically impossible on Steem. According to my sources, Justin had been buying anywhere from 300-500k Steem per day for the voting war; This is a money attack. I am a Co-Founder of a dapp called that was built on Steem. TRON offered us money, power, users and liquidity. We were asked if we could get people to stop voting for the community witnesses and vote for Justin Sun’s witnesses. 3Speak We refused. The vote war raged on; fifteen long and brutal days ensued while the Steem community fought Sun in order to defend the Steem blockchain as long as possible. The community would not compromise with Justin Sun's attempt to centralize the chain. The vast majority of the community banded together to create a new chain resulting in a hardfork called and was deployed to liberate the community from this hostile takeover. HIVE But the war does not end post-fork, at least not in this scenario. It takes 13 weeks to power down on Steem or to " staked tokens, which means the entire community still has 13 weeks of with STEEM before being able to sell their STEEM and be able to rid themselves of their old home that has now been taken over by what is clearly a force for centralization and dictatorship. A sign and testament to the success of the Hive chain and that the community supported and was, in fact, the driving force behind the new chain. unstake’" (like a savings account) "skin in the game" The ensuing battle demonstrates this clearly: The HIVE price began trading, and for the first time in blockchain history sustained a market cap that was higher than its original chain. After a barrage of negative press vs. Justin/Tron for the price of Steem being lower than HIVE, the price of Steem magically starts pumping to surpass the Hive price. When the price of STEEM was higher than HIVE, the top Steem froze whale accounts of several people who are pro Hive allegedly so that they could not dump their STEEM into the market during the artificial price pump. The estimated worth of frozen funds is almost $4 million USD. One chain implodes another chain explodes. Insert HIVE. "witnesses" (controlled by TRON) Learning and adapting is what this technology does best. I believe DPOS is one of the most agile and anti-fragile consensus models out there. Very few blockchains could have survived such an attack that Hive withstood. And to show that the true value moves with the community it shows a high level of censorship resistance of stakehodler value and is a true testament that communities cannot be bought. (the token price of HIVE is worth more than STEEM at the start) Social networks + DPOS is a great combination with robust, agile, and even random events that offer censorship resistance. Hive is winning the war using every tactic in the books, social pressure, migration of the community, projects banding together, and so much more. But its best defense, and this is critical for other chains, was its community. The way the community rallied together and put its differences aside truly made the migration from Steem to Hive a success.  The moral gained in successfully defending and uniting the community gives significant momentum going forwards for Hive to vastly outperform and improve upon its old trajectory as it once was with Steem.  You can see this in the rate at which the public Github for Hive is now being worked on, as well as the improvements in the dapps that have moved to Hive. What doesn't kill you makes you stronger. Because STEEM did not have any smart contracts or a way to make the social agreement between Steemit INC and the community trustless, there was never a hardcoded way to protect vs an exit scam. The HIVE hard fork looks to solve two things, one by moving the development fund (that was purchased by Tron on Steem and used to do the hostile takeover of the Steem blockchain) to a trustless DAO to fund future development of the Hive platform. Also, there are now new ways to combat vs exchange attacks via a 30 day wait period to vote on consensus when you first powerup. In order to achieve this, the Hive chain has made several modifications to its underlying protocol which prevents users who power-up (stake their tokens) from voting on witnesses for one month. Had this solution been implemented into Steem prior to the purchase of Steemit INC. by TRON, the story would have played out much differently, and likely ended in a less traumatic set of events for the Steem community, its stakeholders, businesses, witnesses, dapps, and users. (now Hive) HIVE learned from the former mistakes of STEEM and is a truly community-driven, battle-tested & hardened project. One of the oldest blockchains in crypto just got a rebrand and an upgrade. Most of the major projects on Steem that cared for decentralization moved to HIVE, such as of which I am a co-founder. 3Speak is a censorship resistance, pro-free speech video hosting platform and when posts on Steemit.com as well as on the API level where other front ends were forced to censor their content we were forced to play our hand and commit to operate exclusively on the Hive blockchain. could no longer operate on a centralized blockchain as a censorship-resistant video dapp, where active censorship by the largest stakeholder was prevalent. We require independent block producers (witnesses on Hive) in order to ensure that the possibility of centralization and collusion is minimized to the maximum extent. This is what the Hive blockchain provides for us and our users – free speech and anti-censorship.  Something our users desperately need in this time of censorship and demonetization by the large, centralized media platforms. 3Speak Justin Sun started censoring 3Speak 3Speak Blockchain is code, usually open-sourced, voluntary to run, immutable & the future of how our online world will run. Blockchains adapt and grow to find flaws in governance, it acts like a living organism. While blockchain is in fact, layer one, what is most important is layer zero, the community. As we saw with the STEEM hostile takeover, the true power of blockchain is combining a strong community foundation with solid code to match. Justin Sun would have been better off forking STEEM, for free, than all the negative press, money lost and headache trying to force a community into a centralized authoritarian vision for the chain. What happened was that most of the community left, all the top witnesses left, as well as the infrastructure, and a giant brain drain ensued and poured into HIVE. Users were and still are eager to dump their Steem for Hive. We saw out of the gate that HIVE trades at a higher market cap then STEEM and still does three weeks later. When you have a money attack on a blockchain with a very strong layer zero, the community then gets to leap off the shoulders of the attacker giving the new coin more value. It creates negative sell pressure on the attacker's chain and positive buy pressure on the new community forked chain. But in the case of Steem vs Hive, they targeted Hive whale accounts with 2 Million + Steempower. Most users are still cashing out. It's like Justin Sun trying to stop the ocean with a bucket. There is about 250 million Steem out of the ~ 350 million tokens in total are powered up. And currently, 95%+ of their owners have already initiated a powerdown procedure. (Of course, that’s only true if the former centralized chain allows it you to send tokens). As an investor, my Hive + Steem is worth more than my Steem alone was for the last year or so. What this shows is that a money attack on a blockchain with a strong layer zero only serves to enrich the token hodlers. And most of this money attack on Steem came from a large stake purchase via a cheaply priced, over the counter, the one-time deal between Ned Scott, the former Steemit inc CEO and Justin Sun. On the Hive blockchain, this type of Sybil attack will not be possible, since the ability to accumulate an equivalent sized stake on Hive to that which Steemit Inc has on Steem will have to be done via open market purchases of HIVE.  This would drive the price of HIVE significantly higher and cost the attacked an order of magnitude more to achieve than it did on Steem.  It would also enrich the Hive Token Holders and allow them significant resources and time to then set up and move to another fork, while at the same time damaging the reputation and resources of the attacker. Money attacks boost the attacked token’s price by forced buying, and the forked community chain gets sympathy and support from outside sympathizers looking in. This whole element causes a whirlwind effect wreaking havoc on the attacker's coin while finically enriching the new coin and giving it tons of free publicity in the process. DPOS is a very robust concept and if used correctly, can serve to be very resistant to hostile takeover attacks while proving to be a nice store of value during the attack. The attacks only serve to make an already strong DPOS even stronger. The Hive community blockchain is now thriving. With over 50-100+ community developers, professional marketers, and people from all backgrounds, (even former Steemit INC. members that quit in protest to the Steem hostile takeover are chipping in here and there on Hive). Hive has become one of the most robust, decentralized and censorship-resistant blockchains on the planet. With renewed vigor, many see the large funded DAO as a foundation in the future, where we know now, in a trustless way, HIVE will always have future funding. There is nothing a human mind can come up with that can't be built on Hive. It's the perfect host for businesses/gaming, you name it. The story ended well, we encourage you to ! or for blogging content & for video content. join us on Hive PeakD Hive.Blog 3Speak.Online

Inside Tron's Steem Takeover Attempt and the Birth of the Hive Blockchain

This article describes one of the many ways to import data into AWS DynamoDB database. The option explained here uses Amazon EMR and Hive. Using Amazon EMR and Hive you can quickly and efficiently process large amounts of data, such as importing data from Amazon S3 into a DynamoDB table. What this example accomplishes? Every day an external datasource sends a csv file with about 1000 records to S3 bucket. A lambda function that will get triggered when an csv object is placed into an S3 bucket. Lambda function will start a EMR job with steps includes: Create a Hive table that references data stored in DynamoDB. Creating a hive table that references a location in Amazon S3. Load data form S3 table to DynamoDB table. The following diagram shows the architecture of the process. Prerequisites Basic understanding of CloudFormation. Basic understanding of EMR. Setup an AWS account. Install . Serverless Framework Now, Let’s start Before getting started, Install the Serverless Framework. Open up a terminal and type npm install -g serverless. There is a yml file (serverless.yml) in the project directory. Let’s start to define a set of objects in template file as below: S3 bucket There are 2 S3 buckets, is for EMR logs, is to store csv files. LogBucket S3BucketCsvimport - ServerSideEncryptionByDefault: Resources: LogBucket: Type: AWS::S3::Bucket Properties: AccessControl: Private S3BucketCsvimport: Type: AWS::S3::Bucket Properties: AccessControl: Private BucketEncryption: ServerSideEncryptionConfiguration: SSEAlgorithm: AES256 BucketName: ${self:custom.csvImportBucketName} DynamoDB table A DynamoDB table to load csv data from S3. Resources: ContactsTable: Type: AWS::DynamoDB::Table Properties: TableName: ${self:custom.contactsTable} SSESpecification: SSEEnabled: true AttributeDefinitions: - AttributeName: id AttributeType: S KeySchema: - AttributeName: id KeyType: HASH ProvisionedThroughput: ReadCapacityUnits: ${self:custom.tableThroughputs.${self:provider.stage}} WriteCapacityUnits: ${self:custom.tableThroughputs.${self:provider.stage}} StreamSpecification: StreamViewType: NEW_AND_OLD_IMAGES Lambda function configuration Adding lambda function configuration to serverless.yml. it will be triggered by S3 new objected created event, the lambda function will then start a EMR job flow to process data importing. startEMRJob: handler: src/handler.start_emr_job environment: CONTACTS_TABLE: ${self:custom.contactsTable} SUBNET_ID: ${self:custom.vpc.subsetId} EMR_LOGS_BUCKET: Ref: LogBucket CSV_IMPORT_BUCKET: ${self:custom.csvImportBucketName} events: - s3: bucket: ${self:custom.csvImportBucketName} event: s3:ObjectCreated:* rules: - prefix: uploads/ - suffix: .csv existing: true IAM role We also need to create IAM role for the lambda function, so our lambda function has permission to start EMR job flow. iamRoleStatements: provider: - Effect: "Allow" Action : - "iam:PassRole" Resource : - arn:aws:iam::#{ AWS ::AccountId}:role/EMR_DefaultRole - arn:aws:iam::#{ AWS ::AccountId}:role/EMR_EC2_DefaultRole - Effect: "Allow" Action : - "elasticmapreduce:RunJobFlow" Resource : "*" - Effect: "Allow" Action : - "s3:PutObject" Resource : - "Fn::Join" : - "" - - "arn:aws:s3:::" - ${ self :custom.csvImportBucketName} - "/*" - Effect: "Allow" Action : - "dynamodb:*" Resource : - "Fn::GetAtt" : [ContactsTable, Arn] - "Fn::Join" : - "/" - - { "Fn::GetAtt" : [ContactsTable, Arn] } - "index/*" Adding lambda function Let’s add a lambda function to create an AWS EMR cluster and adding the step details such as the location of the hive scripts, arguments etc. We can use the boto3 lib for EMR, in order to create a cluster and submit the job dynamically from lambda function. logger = logging.getLogger(__name__)
logger.setLevel(logging.INFO) put_dump_record_to_db()
 put_step_scripts_to_s3()
 cluster_id = emr.run_job_flow( Applications=[
 { },
 { },
 { },
 { },
 { },
 { },
 ],
 Instances={ { },
 { }
 ], },
 Configurations=[
 { }
 },
 ],
 Steps=[
 { }
 },
 { }
 },
 { }
 },
 { }
 }
 ], ) logger.error(e) table.put_item( root_path = Path(__file__).parent.parent import boto3 import logging import os from datetime import datetime from pathlib import Path emr = boto3.client( 'emr' ) s3 = boto3.resource( 's3' ) dynamodb = boto3.resource( 'dynamodb' ) def start_emr_job ( event, context ): try : Name= 'test_emr_job' , LogUri= "s3://{}" . format (os.environ[ 'EMR_LOGS_BUCKET' ]), ReleaseLabel= 'emr-5.18.0' , 'Name' : 'Hadoop' 'Name' : 'Livy' 'Name' : 'Pig' 'Name' : 'Hue' 'Name' : 'Hue' 'Name' : 'Hive' 'InstanceGroups' : [ 'Name' : "Master nodes" , 'Market' : 'ON_DEMAND' , 'InstanceRole' : 'MASTER' , 'InstanceType' : 'm1.medium' , 'InstanceCount' : 1 , 'Name' : "Slave nodes" , 'Market' : 'ON_DEMAND' , 'InstanceRole' : 'CORE' , 'InstanceType' : 'm1.medium' , 'InstanceCount' : 2 , 'KeepJobFlowAliveWhenNoSteps' : False , 'TerminationProtected' : False , 'Ec2SubnetId' : os.environ[ 'SUBNET_ID' ], 'Classification' : 'hive-site' , 'Properties' : { 'hive.execution.engine' : 'mr' 'Name' : 'creating dynamodb table' , 'ActionOnFailure' : 'CONTINUE' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step1.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'DYNAMODBTABLE={}' . format ( os.environ[ "CONTACTS_TABLE" ])] 'Name' : 'creating csv table' , 'ActionOnFailure' : 'CONTINUE' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step2.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'INPUT=s3://{}' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'TODAY={}' . format ( datetime.today().strftime( '%Y-%m-%d' ))] 'Name' : 'adding partition' , 'ActionOnFailure' : 'CONTINUE' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step3.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'INPUT=s3://{}' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'TODAY={}' . format ( datetime.today().strftime( '%Y-%m-%d' ))] 'Name' : 'import date to dynamodb' , 'ActionOnFailure' : 'TERMINATE_CLUSTER' , 'HadoopJarStep' : { 'Jar' : 'command-runner.jar' , 'Args' : [ 'hive-script' , '--run-hive-script' , '--args' , '-f' , 's3://{}/scripts/step4.q' . format ( os.environ[ 'CSV_IMPORT_BUCKET' ]), '-d' , 'TODAY={}' . format ( datetime.today().strftime( '%Y-%m-%d' ))] VisibleToAllUsers= True , JobFlowRole= 'EMR_EC2_DefaultRole' , ServiceRole= 'EMR_DefaultRole' , logger.info( 'cluster {} created with the step...' . format ( cluster_id[ 'JobFlowId' ])) except Exception as e: raise def put_dump_record_to_db (): table = dynamodb.Table(os.environ[ "CONTACTS_TABLE" ]) if table.item_count == 0 : Item={ 'id' : 'NA' , 'full_name' : 'demo user' , 'gender' : 'M' , 'address' : 'NA' , 'language' : [ "English" ]}) def put_step_scripts_to_s3 (): scripts = [ "scripts/step1.q" , "scripts/step2.q" , "scripts/step3.q" , "scripts/step4.q" ] for script in scripts: s3.Bucket(os.environ[ 'CSV_IMPORT_BUCKET' ]).upload_file( '{}/{}' . format (root_path, script), script) Hive scripts And finally…let’s add Hive script. Step 1. Creating mapping between Hive and S3 We will create an external Hive table that maps to the csv data file. dynamodb_contacts ( TBLPROPERTIES -- Create external table for dynamodb table. CREATE EXTERNAL TABLE IF NOT EXISTS id string ,full_name string , email string , gender string ,address string , language array < string >) STORED BY 'org.apache.hadoop.hive.dynamodb.DynamoDBStorageHandler' ( "dynamodb.table.name" = "${DYNAMODBTABLE}" , "dynamodb.column.mapping" = "id:id,full_name:full_name,email:email,gender:gender,address:address,language:language" ); Step 2. Creating mapping between Hive and DynamoDB Establish a mapping between Hive and the Features table in DynamoDB. TBLPROPERTIES ( -- Create table using csv in s3 CREATE EXTERNAL TABLE IF NOT EXISTS csv_contacts ( id string ,first_name string , last_name string , email string , gender string , address string , language array < string > ) PARTITIONED BY (created_date string ) ROW FORMAT DELIMITED FIELDS TERMINATED BY ',' COLLECTION ITEMS TERMINATED BY ',' LOCATION '${INPUT}/uploads/' 'serialization.null.format' = '' , 'skip.header.line.count' = '1' ); Step3. Load the table with data in S3 Add current date as partition and load table with csv data in S3. -- add current date as partition ALTER TABLE csv_contacts add IF NOT EXISTS PARTITION (created_date= '${TODAY}' ) LOCATION '${INPUT}/uploads/created_date=${TODAY}/' ; Step4. Import a table from Amazon S3 to DynamoDB Using following Hive script to write data from Amazon S3 to DynamoDB. -- Import csv data to DynamoDB table INSERT OVERWRITE TABLE dynamodb_contacts SELECT DISTINCT id , CONCAT_WS ( ' ' ,first_name,last_name),email, gender, address, language FROM csv_contacts WHERE created_date >= '${TODAY}' that Amazon EMR operations on a DynamoDB table count as read/write operations, and are subject to the table’s provisioned throughput settings. for more details please visit . Note EMR document Now let’s deploy the service and test it out! $sls deploy --stage dev After script deployed, copy a csv file to S3 bucket with prefix, eg. created_date={CURRENT_DATE} $aws s3 cp csv /contacts.csv s3:/ /myemr.csv.import.dev/u ploads /created_date=2020-02-03/ contacts.csv Then we can go to AWS EMR console and check the progress of the EMR steps. It will take several minutes to complete all the steps and cluster will terminate automatically after running above steps. Next We can go to AWS DynamoDB console to verify that the data has been loaded into DynamoDB: Eventually Importing process took about 6 mins to load 1000 records total 76kb into DynamoDB table with write capacity units 10 and no auto scaling enabled. That’s about it, Thanks for reading! I hope you have found this article useful, You can find the complete project in my . GitHub repo

Importing DynamoDB Data Using Apache Hive on Amazon EMR

As referenced to in one of my previous posts, my never-ending frustration regarding fraudulent ICOs led me to an innovative project called Blockhive, and immediately, I was hooked on reading what they had to offer. The Blockhive team believes that there is a need for an alternative to ICO due to the following shortcomings. The token economy is based on the demand, and sometimes selling tokens doesn’t make sense because the token has no real function for your business. While the term certainly sounds a bit daunting, its a fairly simple concept to grasp, and utilizes in their full glory. ILP smart contracts As opposed to an ICO, the ILP raises funds for businesses in the form of a and legally binds borrowers and creditors in via a smart-contract. crypto-loan, In fact, at the end of going through Blockhive’s white-paper, I tried to reason with myself as to why wasn’t THAT the procedure for conducting ICOs. There’s a reason why traditional loan agreements are a problem. Procedures. Hundreds of time-wasting, paper-based procedures. When issuers pay interest and principal, they need to know the KYC and bank account of the Creditors. When Creditors wish to transfer their rights to others, the issuer needs to know the amount of the loan, the date of transfer, and the KYC of the new Creditor. Herein comes Blockhive, which offers a legally binding digitized loan agreement, called an (ILP). Initial Loan Procurement With this innovative loan structure, companies can now that they needs to expand its ecosystem while allowing from the money they have loaned. procure loans creditors to earn interest All KYC information and transactions are recorded on , and all money transactions will be automatically executed via smart contract. blockchain How? Using the Future Loan Access Tokens (FLAT) - HIVE. Distributed to creditors when they commit to an ILP, and receive value of the loan agreement they signed. 1 Hive token per €0.045 A total of 1 billion HIVE tokens would be issued by Blockhive, all to be used for funding projects. Here’s a gist of how it works — As shown, HIVE is embedded with a logical, step-by-step process which considers balances, and interest pay-outs. As yearly interest, Creditors will receive 20% of profits generated by the blockhive community using the platform. Interest is paid in ETH. From the blockhive whitepaper - If project A generates profit of 2,000 ETH and project B generates profit of 3,000 ETH, total profit is 5,000 ETH, and the total amount of interest is 1,000 ETH (5,000 ETH x 20%), to be allocated via to Creditors. smart contracts Thus, Blockhive greatly succeeds in providing an integral feature to the market — cryptocurrency A token which adds value. Take part in the Blockhive ILP To me, Blockhive is an iteration of the ICO, and represents a path where the industry might go in the future. It is a first test case of this new funding method. For all related check out the site here — https://blockhive.ee

HIVE Tokens- A New Approach To Token Utility

designed for data lakehouse architectures, enabling the organization of data on data lakes like tables found in traditional databases and data warehouses. This innovative table format provides a crucial abstraction layer, allowing users to leverage database-like features on their data lakes. Its key features are ACID transactions, which ensure data integrity and consistency, time travel capabilities allowing users to access historical data snapshots, and robust table evolution mechanisms for managing partitions and schema changes. By integrating these functionalities, transforms data lakes into more structured and manageable environments, facilitating advanced data analytics and management tasks. Apache Iceberg is an open-source table format Apache Iceberg The Role of Catalogs in Apache Iceberg The catalog mechanism is a cornerstone in the functionality of Apache Iceberg tables, providing a crucial layer of organization and accessibility, even though the specifics of its implementation are beyond the Iceberg specification. A catalog in Iceberg serves as a registry for tables, tracking Iceberg tables to ensure they are discoverable by compatible tools, thereby facilitating seamless integration and usage. Moreover, catalogs are instrumental in maintaining a consistent view of the data, which is essential for the integrity and reliability of ACID transactions. By acting as the source of truth, catalogs enable concurrent transactions to reference them and . This mechanism is vital for maintaining data consistency and ensuring that the system provides ACID guarantees, thereby enhancing the robustness and reliability of data operations within the Iceberg ecosystem. ascertain the status of other ongoing transactions, effectively determining if another transaction was committed while theirs was in progress Challenges with Catalogs and Choosing the Right Catalog For you When selecting a catalog for Apache Iceberg, several key factors should guide your decision: compatibility with your current tools, the additional functionalities or integrations the catalog offers, and its maintainability. It’s crucial to choose only one catalog for managing your tables because, upon the completion of a transaction, only the active catalog is updated. Utilizing multiple catalogs could result in them referencing outdated table states, leading to consistency issues. If there is an absolute need to employ multiple catalogs, a feasible approach is to designate a single catalog for write operations while others are used solely for reading. However, this setup demands the implementation of custom systems to synchronize the read-only catalogs with the primary to ensure they reflect the most current table state, maintaining consistency to the greatest extent possible. Service and File-System Catalogs The distinction between a catalog in Apache Iceberg is fundamental to understanding their operational dynamics and use cases. Service catalogs, which constitute the majority, involve a running service that can be either self-managed or cloud-managed. These catalogs utilize a backing store to maintain all references to Iceberg tables, with locking mechanisms in place to enforce ACID guarantees. This setup ensures that when a table is modified, the catalog updates references accurately, preventing conflicting changes from being committed. service catalog and a file-system On the other hand, the “Hadoop Catalog” represents a file-system catalog that is compatible with any storage system. Unlike service catalogs, the Hadoop Catalog does not rely on a backing store but instead uses a file named “version-hint.text” on the file system to track the latest version of the table. This file must be updated whenever the table changes. However, since not all storage systems provide the same level of atomicity and consistency in file replacement, this method can lead to potential inconsistencies, especially in environments with high concurrency. Therefore, while the Hadoop Catalog might be suitable for evaluating Iceberg’s capabilities, it is generally not recommended for production use due to these potential consistency issues. A tour of Apache Iceberg Catalogs In the following section, we will delve into the diverse range of currently existing catalogs available within the Apache Iceberg ecosystem. Each catalog offers unique features, integrations, and compatibility with different data storage systems and processing engines. Understanding the nuances of these catalogs is crucial for architects and developers to make informed decisions that align with their specific data infrastructure needs and operational goals. We’ll explore a variety of catalogs, including those that are widely adopted in the industry as well as emerging options, highlighting their respective advantages, use cases, and Nessie that extends beyond the traditional catalog capabilities in the Apache Iceberg ecosystem, introducing . This catalog not only tracks table metadata but also allows users to capture commits at a holistic level, enabling advanced operations such as multi-table transactions, rollbacks, branching, and tagging. These features provide a new layer of flexibility and control over data changes, resembling version control systems in software development. Nessie is an innovative open-source catalog git-like features to data management Nessie can be either self-managed or cloud-managed, with the latter option available through the Dremio Lakehouse Platform. Dremio integrates Nessie into its , offering a seamless experience that includes automated table optimization alongside Nessie’s robust cataloging capabilities. This integration underscores Nessie’s versatility and its potential to enhance data governance and management in modern data architectures. Dremio Cloud product Hive The Hive catalog offers a seamless integration pathway for organizations already utilizing Hive in their data architectures, allowing them to leverage their existing Hive metastore as an Apache Iceberg catalog. This integration facilitates a smooth transition to Iceberg’s advanced features while maintaining compatibility with the existing Hive ecosystem. By using the Hive catalog, users can avoid the redundancy of maintaining separate metadata stores, streamlining their data management processes. However, for those not currently using Hive, adopting the Hive catalog would necessitate setting up and running a Hive metastore service. This requirement introduces an additional layer of infrastructure that might be better served using an option with additional features. REST The REST catalog represents a unique approach in the Apache Iceberg ecosystem, serving not as a standalone catalog but as a universal interface that can be adapted for any catalog type. It is based on an OPEN API specification that can be implemented in any programming language, provided that the specified endpoints are adhered to. This flexibility allows for the creation of custom catalogs tailored to specific use cases, enabling developers and organizations to contribute new catalog types to the community without the need to develop bespoke support for a myriad of engines and tools. Among the catalogs utilizing this specification are Tabular, which offers a “headless warehouse” solution; Unity Catalog from Databricks, which primarily manages Delta Lake tables but also provides Iceberg table access through its UniFormat feature; and Gravitino, an emerging open-source catalog project. The community’s vision is that all catalogs will eventually interface through this REST specification, simplifying tool integration by requiring only a single interface. However, it’s important to note that the specification is still evolving, with version 3 under discussion and development, which is anticipated to introduce additional endpoints for greater extensibility and the incorporation of custom behaviors. AWS Glue The AWS Glue catalog is an integral component of the AWS ecosystem, providing a fully managed catalog service that integrates seamlessly with other AWS tools such as Redshift and AWS Athena. As a native AWS service, it offers a streamlined experience for users deeply embedded in the AWS infrastructure, ensuring compatibility and optimized performance across AWS services. A notable feature of the AWS Glue catalog is its support for auto-compaction of tables through the AWS Lake Formation service, enhancing data management and optimization. While the AWS Glue catalog is an excellent choice for those committed to the AWS platform, organizations operating in on-premises environments or across multiple cloud providers might benefit from considering a cloud-agnostic catalog to ensure flexibility and avoid vendor lock-in. Snowflake Catalog The Snowflake Iceberg catalog offers a unique integration, allowing Snowflake to manage Apache Iceberg tables that are stored externally on your storage system. This integration aligns with Snowflake’s robust data management capabilities while offering the cost benefits of utilizing external storage. However, there are limitations to consider: all table creation, insertions, updates, and deletions must be conducted within Snowflake, as the Snowflake SDK currently only supports reading operations from Spark. While this setup allows users to leverage some cost savings by storing tables outside of Snowflake, it comes with a trade-off in terms of flexibility. Users do not have access to the full range of open-ecosystem tools for managing Snowflake-managed Iceberg tables, which could be a significant consideration for organizations that rely on a diverse set of data tools and platforms. LakeFS Catalog Initially, there was a compatibility issue between LakeFS, a file-versioning solution, and Apache Iceberg due to a fundamental difference in their design: Iceberg relies on absolute paths in its metadata to reference files, whereas LakeFS uses relative paths to manage different file versions. To bridge this gap, LakeFS introduced its own custom catalog, allowing it to integrate with Apache Iceberg. However, as with any custom catalog, there’s a dependency on engine support, which, at the time of writing, appears to be limited to Apache Spark. While versioning is a powerful feature for data management, users looking to leverage versioning for their Iceberg tables might find the built-in table versioning features of Iceberg or the catalog-level versioning offered by Nessie to be more universally compatible and supported options, especially when considering broader ecosystem integration beyond LakeFS’s Iceberg catalog. Other As the Apache Iceberg community evolves, there’s a noticeable shift away from certain catalogs, primarily due to concerns like maintenance challenges or inconsistent implementations. Two such catalogs are the JDBC catalog and the DynamoDB catalog. The JDBC catalog, which enabled any JDBC-compatible database to function as an Iceberg catalog, is seeing reduced usage. This decline is likely due to the complexities and variances in how different databases implement JDBC, potentially leading to inconsistencies in catalog behavior. Similarly, the DynamoDB catalog, initially used in the early stages of AWS support for Iceberg, is also falling out of favor. The community’s pivot away from these catalogs underscores a broader trend towards more robust, consistently supported, and feature-rich catalog options that align with the evolving needs and standards of Iceberg users and developers. Conclusion Apache Iceberg stands as a transformative force in the data lakehouse landscape, offering a structured and efficient way to manage data on data lakes with features traditionally reserved for databases and data warehouses. The journey through the diverse world of Apache Iceberg catalogs highlights the importance of these components in ensuring data accessibility, consistency, and robust transactional support. From the integration-friendly Hive catalog to the innovative Nessie catalog that brings git-like versioning to data, each catalog serves a unique purpose and caters to different architectural needs and preferences. As we’ve explored, choosing the right catalog is crucial, balancing factors like compatibility, functionality, and the specific context of your data ecosystem. Whether you’re deeply embedded in the AWS infrastructure, leveraging the AWS Glue catalog, or exploring the versioning capabilities of LakeFS or Nessie, the decision should align with your strategic objectives and operational requirements. The evolution away from certain catalogs, like the JDBC and DynamoDB catalogs, underscores the community’s drive towards more reliable, feature-rich, and consistent catalog implementations. This shift is a testament to the ongoing maturation of the Iceberg ecosystem and its users’ commitment to adopting practices and tools that enhance data reliability, scalability, and manageability. As Apache Iceberg continues to evolve, so too will its ecosystem of catalogs, each adapting to the emerging needs of data professionals seeking to harness the full potential of their data lakehouses. Embracing these tools and understanding their nuances will empower organizations to build more resilient, flexible, and efficient data architectures, paving the way for advanced analytics and data-driven decision-making. Also published . here

Navigating Apache Iceberg: A Deep Dive into Catalogs & Their Role in Data Lakehouse Architectures

Since 2018, I’ve identified hundreds of new projects tinkering with cryptosocial media applications. Many of them have died. Others are on the brink of falling off a cliff. Still, many more are alive and kicking. What I’m calling cryptosocial media goes by other names, but I prefer media as an all-embracing term. The reason is simple: The basic framework is still social media. The twist is the marriage of cryptocurrencies to social media. And it looks different from platform to platform. cryptosocial Some of the platforms are social blogging platforms. Similar to Medium or Blogger, but closer to Medium as the Big M includes a monetization feature—weak, but it’s still a monetization feature. Crypto-monetization protocols are mostly superior because they include economies that lead to variation in values of the native cryptocurrency based on supply and demand and other factors. Other cryptosocial platforms are more like social networks, but they may include additional features such as an NFT marketplace, a decentralized exchange (DEX), or gaming applications. If they don’t now, they are planning them for the future. If a cryptosocial platform doesn’t make it onto this list, it doesn’t mean it has no value. It could mean that I don’t have enough experience with it to judge it adequately. I have tried every one of the following platforms and can vouch for three things about them: They’re attractive enough visually to not scare me away; Their monetization protocol seems fair and adequate enough to lead to at least a decent side hustle; And they’re easy to use That last one is very important. If a platform isn’t easy to use, it doesn’t matter whether you can earn good money from it or if it’s beautiful. To onboard non-crypto enthusiasts, cryptosocial platforms will have to make their websites as easy to use as Facebook and Twitter. Otherwise, they’ve lost the very crowd that could catapult them to stardom. Now, without further ado, here are 6 cryptosocial platforms I use and don’t mind recommending that you use. These are not in order of most value or highly recommended. One caveat: 6 Cryptosocial Platforms Worth Joining Right Now These 6 cryptosocial platforms are worth a look if you’re interested in earning cryptocurrencies with your time rather than with your money: - Bastyon has a very simple interface. Not only that, it looks like a social media website. I’ve been on Bastyon just a few short days and have already seen more engagement on my posts than I have other cryptosocial platforms that I’ve participated on for months. The native cryptocurrency is PKOIN, or Pocketcoin. Built on its own blockchain network, PKOIN doesn’t cost a fortune to move like many ERC-20 tokens. That’s a plus if you don’t intend to keep your coins on-platform. Another plus is there are lots of categories in which to post your content so the site isn’t geared toward crypto enthusiasts. That’s a huge plus if you have friends who aren’t into crypto but would be willing to try a cryptosocial platform as an alternative to Facebook and Twitter. Bastyon calls itself “The first censorship-resistant social network protocol,” which isn’t exactly true because other cryptosocial platforms that are both decentralized and censorship-resistant have preceded it, but it appears to be both decentralized and censorship-resistant. What’s really cool about it, in my opinion, is that you can post a short post in Facebook-like fashion, a long blog-like post, or upload a video. However, to post videos, you have to have a minimum of 5 PKOIN in your account. Bastyon has most of the features, including private chat, that a good social network must have and users can earn a cryptocurrency that seems to be . Find me on Bastyon . Bastyon only holding its value here - Hive is a hard fork. As such, it’s essentially a Steemit clone and falls into the social blogging category. That’s neither good nor bad, really, but it’s worth noting. Because it is a hard fork, it makes Hive a pioneer among cryptosocial platforms because many of the witnesses broke off from Steemit to launch Hive and Steemit was the first cryptosocial media platform to launch with a cryptocurrency. It is built on its own blockchain and users earn (HIVE) and (HBD), the native stablecoin. Through various decentralized applications (dapps), users can earn a variety of tokens. Like Bastyon, there are plenty of non-crypto categories meaning it’s not all about crypto. However, like Steemit, Hive’s UI is not as simple and intuitive for new users. Posting requires some knowledge of markdown unless you use the visual editor. Still, if you’re persistent, consistent, and add some Hive to your account and stake it to increase your Hive Power, you can do well on Hive. If you join Hive, . Hive Steemit Hive Hive Dollars look me up - Loop began as Trybe, but it rebranded last year and looks or acts nothing like its original. In fact, it is a on a completely different blockchain. It is one of the most attractive cryptosocial platforms I’ve seen, but that isn’t its biggest positive. Loop has a fully operational DEX where users can farm, pool, or stake their earned tokens, buy new tokens, or swap tokens. Loop is built on the Terra blockchain, which means LOOP, LOOPR, and (the native stablecoin) are all tokens, not coins. A planned NFT marketplace is in the works. One thing that makes Loop unique is its rank system. earned are based on one’s Loop rank. In that regard, any one who started as a Trybe member received a 1:1 airdrop of LOOPR tokens last year during the migration, which gives such veteran platform users an advantage. I’m proud to be among them. LOOPR tokens are used only to determine how much LOOP one earns during each month’s airdrop, and I have to say that I’m pleased with the amount of LOOP I’m earning before I stake, farm, or pool them. I have not tried to cash out any of my tokens yet, so I can’t vouch for that process, however, I like what I’m seeing so far. The downside to Loop Markets is that every now and then, there is a technical issue with accessing the website. That can be frustrating, but it doesn’t happen too often. In my opinion, LOOP is undervalued, but UST is on CoinMarketCap. That’s a huge plus. One other downside is my posts don’t see as much engagement as I’d like for the time I’ve put in, but since my earnings aren’t based on engagement, that’s not such a big deal. If you’re ready to try Loop Markets, . Loop Markets completely new project UST LOOP tokens ranked #14 in market cap connect with me - Publish0x is unique in that it has no native cryptocurrency to offer. Users do, however, earn a variety of altcoins that change periodically. Currently, the tokens users can earn are (AMPL) and (STA). Like Hive, Publish0x is a social blogging platform. I do like the engagement I receive on my posts and, unlike Hive, I gather followers without much effort simply by posting awesome content. Another unique feature is that users are paid in tips. If you like an article, you click the tip button and the author receives a majority percentage of one of the payout tokens selected randomly while the tipper receives the default 20 percent. That means you can earn on Publish0x by reading and never writing a post. The tipper can also move a bar to change the tip amount up or down. I’ve received 100 percent tips on some of my articles. Publish0x is also attractive and easy to use, but most of the articles are about crypto. In fact, when I post on non-crypto topics, those articles typically don’t see as much engagement or earn as much crypto as my crypto-focused articles. Of all the cryptosocial platforms that I use, I have the most followers on Publish0x. If you choose to use Publish0x, . Publish0x Ampleforth Statera look me up and connect - There’s not a lot to say about Read.Cash other than it is a social blogging platform on the Bitcoin Cash blockchain. That means the cryptocurrency users earn is (BCH). I’m pleased with my earnings. In a short time, I’ve accumulated a little stash of BCH that seems to be growing, but most of the earnings come through @TheRandomRewarder, the Read.Cash resident reward bot. BCH does have a . There are also a few technical issues that I find annoying, but they are minor. One is the way images are shifted to the bottom of a page when I’m writing a blog post. I just cut the text above the image and remove blank spaces then paste the text below the image to compensate. It adds a few seconds to my posting time, but I believe visual appearance on blog posts is important. Otherwise, Read.Cash isn’t a bad place to post your content, especially if you like BCH. . Read.Cash Bitcoin Cash dust-limit problem Connect with me at Read.Cash - Torum is a true social network. It looks like a social networking platform and acts like a social networking platform, but creators call it a “SocialFi Metaverse” and decidedly aimed it toward crypto enthusiasts. You’ll find non-crypto content on the platform in small amounts, and users can post about anything, but its mostly about crypto. Like Loop, Torum has an NFT marketplace and a DEX, where users can stake tokens and provide liquidity in the XTM/BNB pool. They can also create an NFT avatar. Other features are forthcoming. Torum also includes a native crypto news board that pulls in news from third-party sources, however, the news is so out of date that it’s practically useless. My biggest frustration with Torum is that developers are more concerned with sticking to their roadmap and rolling out planned features than fixing the platform’s annoying bugs. That’s fairly common in the blockchain space. Still, I find it annoying. In that regard, its Clan and Companies features are two unique offerings that are practically useless due to the way Torum rewards users, and developers don’t seem interested in improving that. What I do like is gathering followers is seamless for those who post valuable content, however, Torum does have its spammers. This is one area where developers have taken a proactive approach to improve the platform and they’re not afraid to get rid of the bad apples, though there have been accusations that some users were unfairly targeted as spammers and put in Torum jail for a short time. Users can gift each other, earning Dust for each gift given and Shards for each gift received. These can be used to power up one’s staking multipliers in the DEX. These are the kind of unique features that make a cryptosocial media platform useful, in my opinion. If interested, . Torum connect with me on Torum I’d encourage you to try these cryptosocial media platforms, if you haven’t already. If you have tried some, let me know in the comments which ones you like the most. For more on cryptosocial media platforms and protocols, read my book . Cryptosocial: How Cryptocurrencies Are Changing Social Media Cryptocracy is a decentralized newsletter published 4 times a week. I curate the latest news and crypto analysis from some of the brightest minds in crypto, and sometimes offer a little insightful and snarky commentary. I’m also trying to write one unique original article each week. Always fresh, always interesting, and always crypto. First published at . Not to be construed as financial advice. Cryptocracy Image is a screenshot of Loop Markets.

6 Social Platforms To Help You Connect With Other Crypto Degens

is a productive tool for teams that puts all your actions, messages and files on one simple page. With Hive’s all-in-one dashboard, your team members have their actions prioritized automatically and everything they need to complete them. Hive interviewed , CEO at to know more. Kevin William David John Furneaux Hive Hi John, So tell us about Hive? Hive is a powerful project management platform for teams. It combines messaging, project management, file sharing onto one easy-to-use dashboard. Tell me more about why you are building this? Most tools only cover one area of collaboration — chat, planning, files — and force you to invest in a whole productivity stack. These leaves teams fractured and inefficient. I saw this over and over again as a consultant at and later as global head of customer success at . So I set out with my co-founder to create more integrated platform that is really easy to use. Capgemini Huddle Eric Typaldos That is what we have built in Hive. Manage your to-do list. Check on project progress. Chat with colleagues. Get updates from CRM. All. In. One. Place. It streamlines your workday. It makes it easier to drive projects forward. It’s also deadly easy to use, with lots of flexibility (kanban boards, calendar, lists) and available integrations. What’s unique about what you are building & why do you think companies should use Hive? Some of our primary competitors are , , but we also compete against more established tools like . Asana Basecamp Trello Microsoft Project Unlike these tools, Hive allows teams to plan and execute their projects in one place. So often, a project manager puts together the perfect project plan, but every time something changes they need to resend the file to everyone on the team. In Hive, a manager can make changes to the gantt chart and everyone on the team gets the updates in real-time on their “My Actions” list. Even better, we also have a chat accessible right next to your action tracking. So if someone asks you to do something in chat all you have to do is drag the chat onto your to-do list so you won’t forget. Can you tell us a bit about the different customer segments using Hive? Hive is a great place for any group of people looking for a better way to organize their work. So, we have a diverse set of customers ranging from small tech to large insurance companies to marketing agencies. startups However, Hive has resonated best with , operations, project management and design teams. These are teams with a lot to projects and people to manage at once, who aren’t being served well by any other tools out there. These are also teams that expect the same frictionless user experience they get in all their consumer facing apps. We provide everything they need in one place, and that is why they come to us from some of the other tools they are using. marketing How are your customers using Hive? Could you share a few different use cases? The Pin Factory group at is one great use case. The Pin Factory is responsible for creating pins for sales campaigns. Previously, sales team members would submit request via a which were then transferred to a google sheet and then assigned out by email. After assigned the requests, the team would collaborate and ask questions in slack. They were spread out between 4 different tools, which made it hard to keep track of it all. Pinterest Typeform Hive completely streamlined their workflow. Now, the sales team uses Hive Forms to request new pins. These requests drop straight into the Pin Factory’s Hive workspace and are assigned out with just a click of a button. Any discussions they need to have happen right in the chat on their Hive dashboard. Before they were using 4 different tools to get work done. Now they use one. That is the magic of Hive. Were there any early ‘growth hacks’ or tactics that have contributed to your current success? Integrations have been key for widening our reach. We particularly proud to be one of integrations partners and on the services store. Both organizations have complementary customers, and in these stores we catch them at the decision point. Zenefits WeWork Another thing that was important to us was identifying key verticals and regions we wanted to focus on. This has helped us build a name for ourselves and get more customer referrals. I also think one of the most important factors in our growth has been our focus on customers. We work hard to make sure our customers happy and almost all of our feature improvements have come directly from feedback from customers. Many of our assumptions about what would be helpful have been proven wrong from discussions with our customers. So many companies get caught up in the attitude of “I know better” and I think letting go of that has been key to our success. Our complete focus is on making it easier and more fun to do your work in Hive. What were some of the biggest challenges while building the product early on and how did you solve them? One of the biggest challenges was honing in on the experience that customers wanted. The key for us was setting up customer interviews with our early adopters and watching videos to see where people were struggling and constantly iterating to make it better. We are constantly adding new features or improving user experience. It is definitely a continuous process that will always be apart of the way our company works. FullStory What have been some of the most interesting integrations you’ve added? Are there any that have been particularly impactful for you? We have over 100 integrations available to accommodate all different needs. Some of our most popular , and for files, which allows users to have immediate access to their important documents in Hive. You can also connect your account and share Hive actions in your slack channels. Another integration we are excited about is Zenefits so that when an employee is hired they are automatically added into the company’s Hive workspace. Google Drive Dropbox Box Slack Before we end, what are the top products that you depend to run the company & how do you use them? is the central place where we handle all marketing and sales activities. From the beginning to the end of the customer lifecycle is handled in Hubspot. Hubspot We couldn’t live without . It’s live chat support is critical in helping us provide customers with the best possible experience, and it has really made it easy for a small team to provide best-in-class support. Intercom has been great for helping use understand user behavior and make it easy to identify what changes we need to make. Fullstory is where we handle all our subscription billing. It makes it easy to create invoices and adjust plan details. Chargebee is great for keeping a pulse on customer satisfaction. We can monitor the feedback and quickly identify any weak points so that they can be handled right away. Satismeter Originally published at siftery.com .

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