Sunday, November 28, 2021

Big Data Tools and Technology

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Big Data is becoming an essential requirement for any business. It helps improve decision-making, resolve issues, predict future events, and gain a competitive edge over others. Big Data technologies are in high demand, particularly Apache Hadoop, Cassandra. Organizations are in a constant hunt for professionals skilled in using various big data tools and technologies. Making the most out of the raw data gathered across the organization is very critical. Right tools are essential to handle large amounts of data to identify patterns and trends within them. Professional with knowledge of tools is different from a professional utilizing the tools to the fullest possible extent. Combining the right tools and a good skill set can do wonders and result in exceptional outcomes beneficial to organizations.

Professionals willing to get into the Big Data stream need to equip themselves with many tools. Reliable Data Engineering courses also consist of a section dedicated to these big data tools. Some of the critical tools that one should learn, among others, are listed below.

Hadoop

Hadoop is considered one of the best developments in big data analytics. It has enabled tech companies to store and process large data sets, enhanced computing power tremendously, developed fault-tolerant solutions, lowered the costs with better scalability. Hadoop even pushed for introducing new technology for future developments in big data analytics, such as Apache Spark. Hadoop is a Java-based open-source big data framework that works on the principle of distributed processing. In other words, it splits large data sets and analytics tasks over several nodes leading to smaller workloads that can be run in parallel. It is suitable for both structured and unstructured data to scale up from a single server to thousands of machines.

Key Benefits

  • Scalability
  • Flexibility in the data processing
  • Faster data processing
  • Resilient
  • Cost-effective

HPCC

HPCC stands for High-Performance Computing Cluster, is a big data platform alternative to Hadoop developed by LexisNexis Risk Solution. HPCC is an open-source platform using a single architecture and programming language for data management. HPCC is believed to be more mature and enterprise-ready as it provides additional layers of security, audit, and compliance. HPCC platform uses a programming language known as enterprise control language (ECL) based on C++. ECL is similar to query languages such as SQL. HPCC is a highly efficient big data tool that requires less coding.

Key Benefits

  • Greater scalability and better performance
  • Optimized parallel processing
  • Better redundancy and availability
  • Works well for Complex data processing (Thor cluster)

Storm

Apache Storm is one of the best open source big data tools that offer real-time computation capabilities. It is a simple, free of cost, reliable, fault-tolerant distributed real-time processing system that allows real-time processing of unbounded data streams. Storm can analyze vast data sets with the capability of high data ingestion rates. Storm and Hadoop, both tools are used for big data analytics; however, Hadoop lacks real-time computation, the gap is filled by Storm. Some of the key customers using Storm are Twitter, Wego, Spotify, Alibaba, etc. Storm can be effectively utilized for real-time analytics, uninterrupted computation, machine learning, distributed RPC, ETL, and more. Apache Storm works very fast, easy to set up and operate.

Key Benefits

  • Robust and user-friendly tools.
  • Manages increasing load efficiently.
  • A tool with low latency.
  • Guaranteed data processing

Openrefine

Open Refine is a free, open-source big data analytics application that helps clean and transform messy data. Open source allows one to import data in various formats and change data from one format to another as per requirement. One can use Openrefine with ease for exploring large data sets, and it takes very little time (in seconds) to explore and transform vast amounts of data. Openrefine allows linking and extension. Openrefine dataset with various web services when requested. Openrefine application maintains the data privacy and keeps data on local machines one is working on until one requests it to share and collaborate.

MongoDB

MongoDB is an open-source, document-oriented NoSQL database primarily used for ample volume data storage. It is an advanced and modern database that does not store data in rows and columns as in traditional systems, instead as documents and collections. Documents stored contain data in key-value pairs, while the collections have a function and document sets.

The benefits of using Mongodb are listed below over traditional databases:

  1. MongoDB is very flexible and adaptable as it stores data in the form of documents and is schemaless.
  2. It supports dynamic ad-hoc queries. It allows searching by a field name, regular expressions using document-based on the query language.
  3. It is straightforward to scale and has effective load balancing.
  4. It allows indexing on all fields for improving search quality,
  5. Any data type can be stored, namely integer, strings, Booleans, arrays, and objects.

Some of the key applications wherein MongoDb may be helpful for storing the data includes managing product catalogs and developing mobile applications.

Cassandra

Initially developed by Facebook for their inbox search feature, it is a distributed database management system. Like other big data tools, it is a highly scalable, high-performing, open-source big data tool designed to store vast amounts of data across many commodity servers. The most important feature is a highly available service with no failure. It is best suited for businesses that should not have a single loss.  Cassandra works with all data types, structured, semi-structured, and unstructured, and supports replication over multiple data centers with lower latency.

Big data analytics is an ever-evolving space. One can find scores of tools for each aspect of data analysis, from data cleaning, transforming to visualization. The current article is just a glance at some of the frequently used big data tools. Each tool has its advantages and disadvantages, and one has to select the tool wisely based on their requirement. To consider a tool, one must understand the type of input data, application, scalability, reliability, and various other factors. It is increasingly difficult for one to learn all the tools on their own. If unsure about the tools and techniques, one can look for a training course on big data analytics. Multiple online training courses are available online, which are designed by experts and cover the latest tools used in the space.

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