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The current year is replete with technological innovations that are capable of plotting a vector for the development of the entire industry for 2020 and subsequent years. The AI-operated that solves Rubik’s cube with one hand, the BERT, an advanced voice assistant, that launched a rocket into natural language understanding, smart watches with electrocardiogram sensors embedded – all these 2019 innovations allow us to suggest that the era of digital dystopia is just around the corner. With the tech industry becoming more advanced, cyber threats are expected to be complex and far-reaching. Fortunately, the cyber defence industry is not rest on its laurels, even now there are lots of methods to protect yourself from threats (you can read about it on ). robotic hand Dactyl Cooltechzone Now it is high time to ask: what big tech trends and innovations are going to affect the way we live in 2020? What changes should we be ready for? 5G Network The 5th generation mobile network is expected to provide us with super-fast download and upload speed as well as deliver a much higher degree of flexibility and scalability. Although the technology was launched in 2019, and a whole host of 5G phones have already been announced from such industry giants as Xiaomi, OnePlus, Samsung, and more, the technology is still very limited in terms of locations and mobile networks. A piece of good news is that we can expect more of this in the future. 2020 seems to be a year when 5G networks will come out full force, with improved coverage and more affordable plans. 5G connections are forecast to reach 1.2B by 2025. Apart from lightning-fast Internet speed that makes it possible to stream video and music at high quality, it will enable the development of miscellaneous innovations such as , Virtual Reality, IoT, Augmented Reality. Moreover, it will establish the basis for autonomous vehicles and smart cities. More on this below. Artificial Intelligence New Cyber Cold War in 2020 Leading cyber-security vendors predict that sponsored cyber-attacks will escalate against governments, big businesses, critical infrastructure as far as international tensions increase. What it involves is a real Cyber Cold War funded and enabled by large countries looking to extend their spheres of influence, as we could see it in the secret . cyber strike against Iran Moreover, the creation and propagation of fake news will be escalated as well. But the following year the fake news will be created with the help of Artificial Intelligence which is able to imitate the voice, intonation, speed of utterance in an expert manner, craft high-quality fake videos that even another AI-algorithm will hardly recognize the forgery. Phishing attacks will also become more effective and sophisticated, convincing mobile users to click on malicious links. In these terms, it is crucial to know how to protect your devices from cyber-attacks and malware. One of the most actionable ways is to use a VPN which creates an encrypted tunnel with the strongest cipher. Self-driving cars Throughout 2019 such headlines as “ ” appeared in many famous IT-related resources. Indeed, the idea of autonomous driving is not a utopia anymore, and the question is not “if” but “when”. Waymo’s robotaxi pilot surpassed 6,200 riders in its first month in California Elon Musk’s Tesla is one of the leaders in that industry. Undoubtedly, they achieved great progress in developing self-driving vehicles but they are still far from developing a completely autonomous car by 2020, as had been . So far, all manufacturers are no further than at the third level of autonomy (SAE defined the ), which means that all safety functions are automated, but it is the driver who is responsible for taking over in an emergency. It still requires the driver to be focused on the driving process despite the car doing all the tasks. In order to break new ground and to achieve the fifth level of autonomy, 5G connection with ubiquitous coverage is obligatory. The speed of 4G LTE is not enough for sensors to respond to an emergency case in a matter of milliseconds. promised five levels of automobile autonomy Therefore, it would be premature to assert that completely autonomous cars will be on the market by 2020, but to achieve a transition from the third level of automobile autonomy to the forth one is quite realistic. Well, this technology still has room for growth. AI platform as a service According to , 48% of global CIOs will deploy AI by 2020. Besides, most companies have made their data science a priority and are investing in it heavily. Due to AI impact, companies are expected to produce tens of times more goods and services - a consumer boom is inevitable. Items and devices will become more smart, uniting into ecosystems of the Internet of Things. More and more processes and transactions will be carried out without human intervention, especially with routine mechanical tasks such as collecting data from various sources. Robots will take over this work. A task that a person used to spend hours, days and even months on can be completed by Artificial Intelligence in a few seconds. Gartner Despite AI will continue to become a part of our everyday lives, a lot of businesses will still see the deployment and development of their own AI-based infrastructure as a very expensive thing, so they’d better use AI as a service rather than expand their stuff, hire skilled specialists, and appropriate funds for applying AI to their processes. For example, we can expect that in 2020 every business owner can reach out to Google, Amazon, Microsoft and any other provider in order to implement AI in their business processes, so the provider will supply the algorithms, the infrastructure, the platform, the applications and businessmen will simply pay as a service. This is the massive trend that is going to be bigger in 2020 and is likely to change the whole business structure. Those who recognize that AI is not just about smart devices but about service will have to meet new customers’ demands, unlock new sources of income, and, what is more, thrive in this world of connected things. Blockchain The blockchain tech is a digital ledger that is used for recording transactions but confidential and secure thanks to its decentralized, self-regulated, and encrypted nature. Although the popularity of blockchain tends to wax and wane and the interest is lower now, some innovations go under the radar starting coming out en masse throughout 2020. What are the innovations we may expect from the blockchain technology? Facebook Libra. This is the surprise from Facebook, the social media giant. The concept of brand new revolutionized cryptocurrency emerged in 2019 but it is expected to get launched in 2020. Such market leaders as PayPal, Visa, Mastercard, Uber, eBay, and others are among the investors. Libra is an open-source blockchain that is aimed at revolutionizing the whole world’s economy by producing a crypto coin available to everybody. Despite there are still a lot of regulatory issues that have to be resolved at the moment, Facebook is not going to delay the date of the release. That is means that the following year will be full of surprises for blockchain enthusiasts. Blockchain will be more involved in the gaming industry. Gaming is a huge part of the blockchain economy since the technology is a perfect basis for game developers as it allows them to create peer-to-peer games. Blockchain has already been noticed in the gambling industry. With it, users can play a lot of casino games in a decentralized manner at any time and from any place. Besides, once blockchain will experience more adoption, they will be able to play in a completely anonymous way, so more developers are likely to use this technology in 2020. The tech will be used by governments. The blockchain concept is of interest to government agencies that have to keep, monitor, and administer big data. Most agencies have separate databases within a city’s infrastructure, so they have to request data from other agencies and departments. By using the blockchain tech, they can share data as it circulates across infinite nodes. This makes data management more efficient. Progress does not stand still and with so many changes, it can be really hard to grasp the sheer scale of technology innovations. The trends mentioned above highlights some of the noteworthy innovations but the list if far from being exhaustive.

Top 5 Technology Innovations in 2020 You Have to Be Ready For

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Let's learn about via these 47 free stories. They are ordered by most time reading created on HackerNoon. Visit the to find the most read stories about any technology. Kafka /Learn Repo 1. Kafka Storage Design  -  Making File Systems Cool Again! What makes Kafka so Fast? A Deep Dive into Kafka Storage Internals. 2. Build a Live Dashboard with Materialize, Airbyte, MySQL and Redpanda/Kafka 3. Deploying Apache Kafka With Kubernetes Deploying Kafka on Kubernetes is a low-effort approach to setting up an event-driven architecture to support your API ecosystem in the cloud. 4. Top Apache Kafka® Interview Questions for Juniors In 2023 Prep for an Apache Kafka interview by reading this questions! Aimed at juniors. 5. Comparing Apache Kafka with Oracle Transactional Event Queues (TEQ) as Microservices Event Mesh This blog contrasts and compares transactional and message delivery behavior of Kafka with the converged Oracle DB and Oracle Transactional Event Queues/AQ 6. Kuma 1.0 GA Released With 70+ New Features & Improvements Today is a big day for Kuma! Kuma 1.0 is now generally available with over 70 features and improvements ready to use and deploy in production to create modern distributed service meshes for every application running on multiple clusters, clouds, including Kubernetes and VM-based workloads. 7. Setting up Kafka on Docker for Local Development In a world where data is king, Kafka is a valuable tool for developers and data engineers to learn. 8. A Brief Introduction to Commit Logs Logs are everywhere in software development. Without them there’d be no relational databases, git version control, or most analytics platforms. 9. Key Apache KafkaⓇ Concepts Every Dev Needs to Know In my first few months learning Apache Kafka, I drew up a blog post on the fundamental concepts behind implementing it. 10. Using dbt with Materialize and Redpanda 11. Take your Materialized Views to the Next Level by Joining MySQL and Postgres 12. How to Execute a Scheduled Task in Keycloak on Startup In this article, we will look at how to execute a scheduled task in Keycloak on startup using a Kafka consumer as an example. 13. Kafka Administration and Monitoring UI Tools Kafka itself comes with command line tools that can do all the
administration tasks, but those tools aren’t very convenient because
they are not integrated into one tool and you need to run a different
tool for different tasks. Moreover, it is getting difficult to work with them when your clusters grow large or when you have several clusters. 14. Build your Own Live Chart With Deno, WebSockets, Chart.js and Materialize We will build a simple dashboard app that displays data from a Deno Web Socket server. 15. How to Easily Stream Data From a Headless BI and SQL Engine Now, you can use Cube to build data modeling, caching, and access control layers on top of streaming SQL, just as with cloud data warehouses. 16. Stream and Display Data in Realtime with Materialize and Adonis In this tutorial, we are going to build a web application using AdonisJS and integrate it with Materialize to create a real-time dashboard 17. Auto-generation of Documentation for Event-driven Architecture Auto-generation of documentation for Event-driven architecture 18. Using Kafka & Zookeeper Offsets Kafka version 0.9v and above provide the capability to store the topic offsets on the broker directly instead of relying on the Zookeeper. 19. Using KSQL Stream Processing & Real-Time Databases to Analyze Kafka Streaming Data [A How-To Guide] Intro 20. Apache Kafka: the Complicated Simplicity This post is about issues, misunderstandings and sometimes heroic solutions from our experience of using Kafka as the main data exchange platform. 21. How to Use Materialize and Redpanda to Analyze Raspberry Pi Temperature Data This is a self-contained demo using Materialize to process data IoT devices data directly from a PostgreSQL server. 22. Debezium Introduction: Another Change Data Capture Tool Building an enterprise data warehouse can be either relatively straightforward or very sophisticated. It depends on many factors, such as the conceptual data model complexity and the variety of source systems. In many cases, applying the Change Data Capture (CDC) approach can make the data integration simpler. Fortunately, there are plenty of CDC tools available in the market, many of which are easy-to-use and affordable, while others are cumbersome and expensive (for what it is). 23. Tips About Kafka Connect On Heroku You Can't Afford To Miss Introduction 24. Best Practices For Apache Kafka Configuration Having worked with Kafka for more than two years now, there are two configs whose interaction I've seen be ubiquitously confused. 25. Deploy Your Application with Kafka and Docker in 20 Minutes In this tutorial, we'll walk you through how to use Docker, Kafka, and Kubernetes to deploy a simple application. 26. Data Pipelines and Expiring Dictionaries Designing a data pipeline comes with its own set of problems. Take lambda architecture for example. In the batch layer, if data somewhere in the past is incorrect, you’d have to run the computation function on the whole (possibly terabytes large) dataset, the result of which would be absorbed in serving layer and are reflected. 27. Choosing Between Enterprise Messaging and Event Streaming For Your Architecture Comparing Enterprise messaging and event streaming across different dimensions to see  how they excel at solving different but related messaging problems 28. Lambda Architecture Batch Layer: Visualizing All Time Taxi Data [Part 3] In this part i would be talking about the batch layer of the Lambda Architecture. Batch layer is computed by applying a function to the whole historical dataset, to answer some high level questions which cannot be answered by either speed layer or serving layer. The computations typically take hours or days to run, and the results are stored usually in a distributed file system (although this is not a requirement). For example, the queries that might need to be answered would range from the beginning of the dataset to now, in our case, till date how many cabs have served how many passengers, or what is the total distance driven by all the cabs. In this article i would try to answer questions like these based on the dataset that i have. The code for the article can be found here. 29. Understanding Kafka Partitions: How to Get the Most out of Your Kafka Cluster This blog provides an overview around the two fundamental concepts in Apache Kafka: Topics and Partitions. While developing and scaling our Anomalia Machina application we have discovered that distributed applications using Kafka and Cassandra clusters require careful tuning to achieve close to linear scalability, and critical variables included the number of Kafka topics and partitions. In this blog, we test that theory and answer questions like “What impact does increasing partitions have on throughput?” and “Is there an optimal number of partitions for a cluster to maximize write throughput?” And more! 30. Apache Kafka: How Does It Work? This article was originally posted to the Confluent blog. 31. RocksDB Is Steadily Eating the Database World Technical design. Because one of the most common use cases of the new databases is storing data that is generated by high-throughput sources, it is important that the store engine is able to handle write-intensive workloads, all while offering acceptable read performance. RocksDB implements what is known in the database literature as a log-structured merge tree aka LSM tree. 32. How to Authenticate Kafka Using Kerberos (SASL), Spark, and Jupyter Notebook Kafka & Spark integration may be tricky when Kafka is protected by Kerberos. Here is the guide on how to access Kafka with Spark and Spark Streaming. 33. Lambda Architecture Serving Layer: Real-Time Visualization For Taxi [Part 2] In this part i would be talking about the serving layer of the Lambda Architecture. Serving layer is derived either by performing computation on batch data to arrive at a view that is mid way from speed layer and batch layer 34. The Only Decoupling Checklist You Need To Know About My team has recently successfully decoupled one of the critical business domains of the company. The initial integration had such a tough deadline that the only way to meet it was to add code to the monolith. And… The feature that went from conception to production in three weeks ended up taking almost one year to decouple. 35. Getting Started with Spring Cloud Stream This post was co-written with Ben Wilcock, Product and Technical Marketing Manager for Spring at Pivotal. 36. How Stream Processing Makes Your Event-Driven Architecture Better If you’re an architect or developer looking at event-driven architectures, stream processing might be just what you need to make your app faster, more scalable, and more decoupled. 37. 5 Problems and Their Solutions With Creating a High-Load Service Using .NET and Kafka Specifics and complications of creating a high-load service using .NET and Kafka. 38. Real-time Analytics and Data Processing with Kafka & Spark Real-time analytic systems use data processing frameworks, including Apache Kafka and Apache Spark. Learn more here! 39. Lambda Architecture Speed Layer: Real-Time Visualization For Taxi [Part 1] Lambda architecture has 3 components, a) Speed layer, which is the streaming data layer or real time data layer, b) serving layer, which is the database layer, which is derived by aggregating data from speed layer, and c) batch layer, which is the set of computations which are perfomed on large sets of data, typically stored in a distributed file system. In this post i will be talking about how to implement the speed layer, by visualizing real time taxi data. Post that, the visualization will allow us to make some real time business decisions. Code for this article can be found here. 40. How to Stream XML messages from IBM MQ into Kafka into MongoDB Let’s imagine we have XML data on a queue in IBM MQ, and we want to ingest it into Kafka to then use downstream, perhaps in an application or maybe to stream to a NoSQL store like MongoDB. 41. Kafka Gotchas I’ve assisted several large clients in building a microservices-style architecture using Kafka as a messaging backbone, having a reasonably good understanding of its abilities and the use cases that really bring them out. But I’m not a Kafka apologist by any stretch; any technology that has gone through such a rapid adoption curve is bound to polarise its audience and rub certain developers up a wrong way, and Kafka is no exception. Like anything else, you need to invest a significant amount of time in getting across Kafka and event streaming in general, before you become fully proficient and can harness its might. And be prepared to face one or two frustrations, to put it mildly, along the way. 42. Kafka Connect Framework: Creating a Real-Time Data Pipeline Using CDC Microservices, Machine Learning & Big Data are making waves among organizations. Curiously they all share the same biggest concern: data. 43. Kafka Basics and Core Concepts: Explained In this article we will cover the core concepts of Kafka and also will touch upon a few of the advanced topics. 44. Jaeger Persistent Storage with Elasticsearch, Cassandra and Kafka Running systems in production involve requirements for high availability, resilience and recovery from failure. When running cloud-native applications this becomes even more critical, as the base assumption in such environments is that compute nodes will suffer outages, Kubernetes nodes will go down and microservices instances are likely to fail, yet the service is expected to remain up and running. 45. How the ZooKeeper Solves the Dining Philosophers Problem In this blog by Paul Brebner, Instaclustr's tech evangelist explains the Apache ZooKeeper using the famous dining philosophers problem. 46. Introduction to Event Streaming with Kafka and Kafdrop Event sourcing, eventual consistency, microservices, CQRS... These are quickly becoming household names in mainstream application development. But do you know what makes them tick? What are the basic building blocks required to assemble complex, business-centric applications from fine-grained services without turning the lot into a big ball of mud? 47. 5 Things Every Apache Kafka Dev Needs To Know: A Performance and Architectural Deep Dive Here are five tips on how Kafka works and how you can get started with Apache Kafka. Thank you for checking out the 47 most read stories about Kafka on HackerNoon. to find the most read stories about any technology. Visit the /Learn Repo

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