Essential tools for big data
By: Sajjad Hussain
Tags: Technology, Machine Learning, Big Data, Tech, Tutorial

On the one hand, for many companies, big data is a game changer. It provides insights that we have never opened before. On the other hand, it is impossible to use this information without the right tools. In order to make the most of any big data strategy, it is essential for companies to obtain innovative solutions for managing, mining and understanding data.

Fortunately, there are many developers who are creating the software we need to deploy the data environment. In view of this, we have listed ten essential tools.

Elastic Search

Finding and tracking data is essential to managing data. Elastic Search is one of the most powerful search engines on the market today. As a distributed RESTful analysis engine, this solution helps companies store data centrally, thereby providing simpler information control. You can also set up reliable search functions, including automatic search, fuzzy search and full text search.

Elastic Search is also suitable for multi-tenant systems, so it is a cost-effective solution for companies working on multiple installations of the same main system. Elastic search provides four types of benefits when analyzing and finding the pattern of intelligence

Query: Perform structured, unstructured, measurement, and geographic searches to discover insights. Analysis: zoom out and observe the overall situation, explore data trends. Speed: Provide incredible speed for any business. Scalability: It can run on a personal laptop or on hundreds of servers.

Apache Flume

Flume is a reliable, distributed, and highly fascinating service that collects and aggregates large amounts of data. Apache Flume has a flexible and simple architecture, very reliable and fault-tolerant, although at first glance it does not seem to be the most advanced tool on the market.

Flume is a Hadoop tool that developers can use to collect data streams from various sources and transfer them to a centralized environment. Flume is also very good at managing the stable data flow between various systems. Following are the goals which Apache Flume gives to the data analyst

Align data streams from a range of different sources Access a highly fault-tolerant and reliable failover mechanism Collect data in stream and batch mode Combine social media, sensor information, application logs, etc. Store all data in a central space

Tensorflow

One of the most famous open source machine learning libraries in the world, Tensorflow is Google’s open source artifact for artificial intelligence. As an end-to-end open source platform, Tensorflow can easily turn your data into artificial intelligence fuel. In addition, an integrated ecosystem of community resources, libraries, and tools allows researchers and developers to create the most advanced and largest language applications.

In addition, with the help of tensorflow, companies can find simple ml problem solutions, simple model building functions, and powerful experimental options. The following are the incredible features of Tensor flow

Simple and flexible open source architecture The latest model of machine learning Simple model building Powerful milliliters on premises, in the cloud or on the device A range of resources and community support

Apache Kafka

Kafka is a big data tool approved by Apache to process and manage data in real time. The Kafka platform processes incoming data streams regardless of their destination or source.

With Kafka, the company can handle countless events every day.

Manage record flow Process the data stream as it appears Store information in a durable, fault-tolerant way Access to core APIs to extend Kafka’s capabilities

Apache Cassandra

Apache Cassandra is a distributed database that companies can use to manage a large number of data sets on multiple servers. As one of the best big data tools for managing structured data, Cassandra provides highly available services without any single point of failure.

When you need high availability and scalability without compromising performance, Cassandra is an excellent choice. Cassandra also supports replication across multiple data centers, thus providing users with lower latency.

Fault-tolerant data management For better inner peace, no single point of failure Scalable high-availability data management Choose between asynchronous and synchronous replication Provide third-party services

Apache Flink

Flink is an open source framework. With Flink, enterprises can access distributed stream processing engines to calculate data in unbounded or bounded environments.

In addition, a big advantage of this tool is that it can run in all cluster environments you can think of, including Hadoop, Kubernetes, and Apache Mesos.

Access useful APIs at several levels of abstraction Provide flexible windows Support various third-party connectors Fault tolerance and failure recovery

Apache Spark

Apache Spark, this open source tool fills the gaps in data processing, real-time and batch data processing in Hadoop solutions. Spark is much faster than traditional tools in processing data, which is very good for data analysts.

Spark is an ideal choice for companies that already use Apache solutions (such as Cassandra or Flink). It makes the core of your data processing project more efficient and valuable, and helps with tasks such as scheduling and distributed task transmission.