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6 Popular Big Data Technologies that You Must Know

Do you know what could be the key differentiator between a market leader and a has-been leader?

It is “data management” and any organization that cannot handle the influx of data and put it into good use is likely to give way to wiser companies that know how to make their data work.

They are constantly on the quest for finding new strategies to make innovative use of Big Data. In fact, the power of Big data and mobility can truly elevate businesses to new levels.

Hence, Big Data is the term given to huge amounts of data. As the data comes in from a variety of sources, it could be too diverse and too massive for conventional technologies to handle. This makes it very crucial to have the skills and infrastructure to handle it intelligently.

This data must be analyzed computationally to reveal patterns and trends, thereby aiding in marketing and promotional campaigns. Here are a few examples of organizations that make use of Big Data:

· Government organizations track social media insights to capture the onset or outbreak of a new disease.

· Oil and gas companies integrate their drilling equipment with sensors to ensure safe and more productive drilling.

· Retailers track web clicks to help identify the various behavioral trends to improve their ad campaigns.

Now, let’s look at some of the trendy big data technologies that you can use to promote your business:

1. Apache Spark

With its built-in modules for streaming, machine learning, graph processing and SQL support, Apache Spark certainly deserves a mention as the fastest and general engine for big data processing. It supports all important Big Data languages including Python, Java, R and Scala.

It complements the main intention why Hadoop was initially introduced. The main concern with data processing is speed, so you need something to diminish the waiting time between queries and the time it takes to run the program.

Even though Spark was introduced to speed up the computational computing software process of Hadoop, it is not an extension of the latter. In fact, Spark uses Hadoop for two main purposes only — storage and processing.

Use case: Apache Spark is a major boon to companies aiming to track fraudulent transactions in real time, for example, financial institutions, e-commerce industry and healthcare. Suppose a credit card was swiped for a huge amount, say, Rs. 50,000 whilst your wallet was lost and it wasn’t you who swiped, it is possible to detect where and when the fraud took place.

2. Apache Flink

If you have heard of Apache Spark and Apache Hadoop, then you will have heard about Apache Flink as well. Flink is a community driven open source framework, founded by Professor Volker Markl — Technische University, Germany. Flink meaning “swift” in German is high performing and extremely accurate data streaming.

The capabilities of Flink is inspired by MPP database technology (for functioning like Declaratives, Query Optimizer, Parallel in-memory, out-of-core algorithms) and Hadoop MapReduce technology for functions like Massive scale out, User Defined functions, Schema on Read).

3. NiFi

NiFi is a powerful and scalable tool to possess, thanks to its capacity to store and process data from a variety of sources with minimal coding and a comfortable UI. And that’s not all. It can easily automate the data flow between different systems. If NiFi doesn’t contain any sources that you require, then the straightforward Java code lets you write your own Processor.

The specialization of NiFi is data extraction and is a highly useful solution for filtering data. As NiFi is an NSA project, the security for this tool is commendable.

4. Kafka

Kafka is a must because it is a great glue between various systems right from Spark, NiFi to third party tools. And streams of data can be handled efficiently and in real time. Kafka is open source, horizontally scalable, is fault tolerant, extremely fast and a safe option.

Being a distributed system, Kafka stores the messages (simple byte arrays and developers store any object in any format) in topics, and the topics themselves are partitioned and replicated across different nodes.

When Kafka was first introduced, it was a distributed messaging system built initially at LinkedIn, but now is part of the Apache Software Foundation and is used continuously by thousands of companies.

Use case: Pinterest uses Apache Kafka. The company built a platform called Secor using Kafka, Storm and Hadoop for real-time data analytics to ingest data into MemSQL.

5. Apache Samza

The main purpose of the conception of Apache Samza is to extend the capabilities of Kafka and is integrated with the feature alike Fault Tolerant, Durable messaging, Simple API, Managed State, Extensible, Processor Isolation and Scalable.

It uses Apache Hadoop YARN for fault tolerance and Kafka for messaging. Thus, you can say it is a distributed stream processing framework. And it comes with a pluggable API to run Samza with other messaging systems.

6. Cloud Dataflow

Cloud Dataflow is a native Google cloud data processing service integrated with simple programming model for both batch based and streaming data processing tasks.

With this tool, you no longer have to worry about operational tasks including performance optimization and resource management. Through its fully managed service, it is possible to dynamically provision the resources to maintain high utilization efficiency while minimizing latency.

And you no longer have to worry about programming model switching cost through its unified programming model method. This method aids in batch and continuous stream processing, making it easy to express computational requirements without worrying about data source.

Conclusion

The big data ecosystem is constantly evolving and new technologies come into existence very frequently, many of them evolving further and further beyond the Hadoop-Spark stacks. These tools can be utilized to ensure seamless work with security and management, sans any hiccups.

Data engineers require these tools to pull, clean and set patterns for data to help data scientists explore and examine them thoroughly, and build models.

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