Learn the Tools and Techniques for Effective Data Processing in Big Data Analytics

Data processing: Data processing is the process of transforming the raw data into meaningful information to provide insights. To process large volume of data (big data), a set of programming models are applied to access the large-scale data to extract meaningful insights. Big data is stored in several commodity servers. Hence, traditional models like MPI(Message passing interface) do not suffice.

What makes big data processing effective?

Big data processing has few requirements to be effective. Based on the characteristics, big data requirements can be classified as:

Volume: Volume in big data refers to the enormous amount of data that overwhelms organizations. Today’s businesses are bombarded with data from various sources like social media/networks, transaction processing mechanisms, and many more. The traditional local server storage does not suffice the requirement. Therefore, big data tools and techniques help to split the colossal volume of data into chunks and save across several clusters of servers. We will understand more about such tools and techniques in the later part of this article.

Velocity: The term velocity in big data refers to the speed with which the data is generated, captured, and processed. A few decades ago, it took a while for the data to get processed and to share the right information. But in today’s digital era, real-time data is available which has to be processed with greatest speed. Big data tools and technologies help to monitor and process the enormous data in real-time and help business leaders to take informed, data-driven decisions in a timely manner.

Variety: Variety in big data refers to the digitized data that an organization gets and sends in various formats. It can be structured or unstructured data. It is important to track and interpret this variety of data. This is where the big data tools and technologies come in place. We will understand more about such tools and techniques in the later part of this article.

Veracity: The term Veracity in big data refers to the quality of data and the accuracy of data.  Validation of Small quantity of data is simple and to check for he accuracy and quality is also easy. Unfortunately, large volumes of data is tough to validate and check for accuracy and quality. By the time the data is validated, it may become obsolete. Therefore, big data tools and technologies help in accuracy and maintaining quality of the data. Such data is a boon as it can help to predict consumer preferences and can prevent diseases to many more wonders.

Open source tools and technologies in Big Data processing

Apache Hadoop: Apache Hadoop is a Java-based open-source framework that is used to store and process very large data. The data sets may vary from giga bytes to petabytes. The unique feature of Hadoop is that it enables numerous analytical tasks to run on the same data set. The framework distributes the big data and the analytics across several nodes in clusters and then breaks down into smaller chunks which can be run parallelly. Hadoop has flexible features which allow the storage of structured and unstructured data in any format.

Apache Cassandra: Apache Cassandra is an open-source, NoSQL database which is most suitable for high-speed and online transactional data. This amazing tool can manage enormous amounts of data across commodity servers. The best aspect is that Cassandra provides high availability with no failure point and allows low latency operations for clients. The data distribution across multiple data centers is made easy with replication.

Apache Storm: The Apache storm is an open source distributed real-time computation system and can be used with any programming language. This tool is fault tolerant and has horizontal scalability. Apache storm is a single node processing system and can process the data even when the node is disconnected.

Apache Spark: Apache Spark works on in-memory cluster computing technology. This open-source tool speeds up the processing speed of an application. Apache Spark works on batch applications, interactive queries, iterative algorithms and many more workloads. Spark supports several/multiple languages and provides built-in APIs for Java, Scala, and Python. Apart from supporting Map and Reduce, Spark supports data streaming, SQL queries, machine learning and algorithms.

MongoDB: MongoDB is popular in agile teams. A Mongo database is a non-relational document database that supports huge data storage in a structured manner without disturbing the stack. All the documents can be stored in a schema-less database.

Qubole: Qubole is a big data tool that uses machine learning, and artificial intelligence, and can adapt to a multi-cloud system.  Multi-source data can be migrated to a single location with Qubole. This amazing tool aids in predictive analysis. Qubole also provides real-time insights into moving data pipelines. This reduces the time and effort.

Apache Hive: Apache Hive is a distributed data-warehousing system. This amazing tool facilitates analytics from the central data warehouse.  The analytics function I performed at a large scale on petabytes of data residing in the distributed storage.

KNIME: Konstanz Information Miner (KNIME) is an open-source platform that supports Linux and Windows operating systems.  This open-source big data tool is useful for enterprise reporting, data mining, data analytics and text mining.

High-Performance Computing Cluster (HPCC): High-Performance Computing Cluster (HPCC) is an open-source tool that offers a 369-degree big data solution. This is also called a data analytics supercomputer which is based on Thor architecture. It’s a hugely scalable supercomputing platform.

Integrate.io: Integrate.io is an excellent big data tool that can perform big data analytics on a cloud platform. This amazing scalable cloud platform can function without code or low code capability. This big data cloud platform has a capacity to connect to more than 150 data sources. This is one of the efficient ETL and data transformation tool.

Factors to be considered before selecting the appropriate big data tool:

•          Business objectives
•          Cost
•          Advanced analytics
•          Usage and ease of use
•          Security

Conclusion: There are several tool which are available in the business world. You should learn and understand the tool that is widely used by businesses. Every tool had its own Pros and cons. You should also understand the type of business that business are using and the purpose of analysis. Happy learning.