Top open-source big data tools you must consider for data science projects

In today’s technological and business environment, data is at the core of every decision and insight. Big Data analytics is, in fact, a revolution in the information technology landscape that is projected to generate revenue of $103 billion by 2027. The necessity to gather, process, and convert data is increasing every day with large volumes of unstructured data collected from social media and the web.

Big data, however, cannot be equated with any particular volume of data. Terabytes, petabytes, and even exabytes of data collected over time may be used in big data deployments. To overcome the challenge of processing and analyzing vast datasets, open-source solutions for data science projects have emerged as robust solutions to tackle these data-related complexities. This extensive guide explores essential open-source tools for Data Science projects and offers an overview of seven of the most popular open-source big data tools.

Popular open-source data tools you can consider

The worldwide volume of data has grown exponentially, along with the rise of big data analytics. The big data analytics market is predicted to grow by over $655 billion by 2029. With this growing demand, organizations need to bring efficiency into data analytical tasks, which can be done with open-source data tools. Here are the essential open-source tools for data science projects you must know:

Also Read: Roles to explore after an MBA in Business Analytics

01. Apache Hadoop

Apache Hadoop is one of the most essential and must-consider open-source tools for big data projects. Apache Hadoop is a widely used tool in the big data business for large-scale data processing. Big data analytics relies heavily on Hadoop’s MapReduce programming model, which makes it possible to analyze structured and unstructured data in large batches efficiently.

Features:

• Distributed storage (Hadoop Distributed File System – HDFS)
• MapReduce programming model
• Scalability for big data processing

Uses:

• Analytics and processing of big data
• Data storage and archiving 
• Marketing analytics
• Risk management

Benefits:

• Terabytes of data can be processed in minutes and Petabytes in hours.
• A Hadoop cluster can be expanded simply by adding more nodes.
• The tool is significantly affordable since it is open source and stores data on commodity hardware.
• Replication of data from one Hadoop cluster to other nodes in the system protects against any hardware or software failure.

Project examples:

• Analysis of movie ratings for better movie recommendation
• Analysis of both structured and unstructured data to enhance consumer understanding and service.

02. Apache Spark

Apache Spark is one of the most popular open-source big data tools for analytics for big data workloads. Efficient in-memory data processing engine Apache Spark is renowned for its quick data analytics capabilities. Spark can operate independently, in the cloud, on Apache Hadoop, Apache Mesos, and Kubernetes—as well as against a variety of data sources.

Features:

• Swift processing with 100x faster in memory and 10x faster on the disk
• Dynamic in nature as it provides 80 high-level operators
• Fault tolerance
• Real-time stream processing

Uses:

• Used for real-time processing such as can real live tweets and perform analysis on them 
• Use in graph processing such as social or road networks
• The tool can be used to analyze a patient’s medical history and identify possible health issues

Benefits:

• It is developer-friendly, as it has a simple API that makes it easy to learn and use
• Can handle huge data sets
• Spark offers more than just “MAP” and “reduce.” It supports SQL queries, streaming data, graph algorithms, and machine learning (ML).

Project examples:

• It can predict flight delays

Also Read: Industries You Can Consider after an MSc in Business Analytics

03. Apache Flink

Apache Flink is a powerful open-source stream processing framework that has garnered significant popularity in the big data field in recent years. Large volumes of streaming data can be processed and analyzed in real-time, which makes it a desirable option for modern applications like machine learning, stock market analysis, and fraud detection.

Features:

• Processing capabilities for streams and batches
• Real-time processing
• Sophisticated state management

Uses:

• Used in event-driven applications that automate response to events as they happen and used in industries like finance, healthcare, and more
• Used in business process monitoring and fraud detection

Benefits:

• With very little latency, Apache Flink can scale to thousands of nodes.
• APIs for creating Java and Scala streaming applications are supported by Apache Flink.
• Due to its distributed runtime engine, Apache Flink is an excellent option for mission-critical streaming applications since it guarantees fault-tolerant stream processing and high availability.
• Multiple data formats, including CSV, JSON, Apache Parquet, and Apache Avro, are supported by Apache Flink.

Project examples:

• Examining transactional data and setting up alarms in response to suspicious activity.
• Examining log data to spot possible breaches of security early and take preventative action against cyberattacks.

04. Apache Kafka

Apache Kafka is one of the most essential open-source tools for data science projects and building real-time streaming data pipelines. It is a framework for event streaming that is used to gather, process, store, and integrate data at scale. Apache Kafka facilitates the creation of practical data pipelines for enterprises. To handle the data, it operates as a distributed system of clients and servers that supports the publish-subscribe model.

Features:

• It can process and store events.
• It is possible to integrate Kafka with other systems like AWS, Hadoop, Spark, and Storm.
• It can be scaled and is resistant to errors.
• It provides quicker processing.

Uses:

• Tracking website activities
• Monitoring IoT devices
• Managing supply chains
• Real-time data analytics

Benefits:

• Kafka can readily handle the massive volume of data streams
• It is reliable since it is distributed, partitioned, replicated, and fault-tolerant
• Kafka is incredibly quick and ensures that no data is lost or interrupted

Project Examples:

• Kafka is used by the LinkedIn tech stack for metrics monitoring, activity tracking, and message exchange. 
• It is used by Google Analytics to process data in real-time.

05. Jupyter Notebooks

It is a must-consider open-source tool for big data projects. With Jupyter Notebook, users can create and share narrative text documents, equations, formula visualizations, and live code. For data scientists and analysts, its interactive features are quite helpful.

Features:

• Accommodates a wide range of programming languages
• Combines text, graphics, and code into a single document
• Interactive and collaborative notebook interface

Uses:

• Exploratory data analysis (EDA)
• Data cleaning and transformation
• Data visualization
• Statistical modeling
• Machine learning

Benefits:

• Outstanding for presenting and exchanging data science work
• Incredibly flexible and engaging
• Ideal for investigating and analyzing data
• Integration with a range of libraries for data visualization

06. Apache Cassandra

A distributed database management system called Cassandra is made to manage large amounts of structured data among commodity computers. The Apache non-profit organization is in charge of this NoSQL data storage system, which uses a distributed design to provide high availability, scalability, and reliability.

Features:

• Rather than being a centralized system, Cassandra is well-distributed and designed to operate across several nodes.
• Cassandra scales elastically. That means there won’t be much resistance or trouble when scaling it up or down.
• The Cassandra Query Language, or CQL, was introduced by Cassandra. It’s an easy interface to access Cassandra.

Uses:

• IoT data management
• Social media data management
• Authentication and fraud detection
• Logistics and asset management

Benefits:

• It can deliver fast and consistent results
• It is free and can be modified by anyone
• It is flexible and can accept different types of data and schema

Project examples:

• Cassandra is used by Twitter for analytics
• It serves as Mahalo’s main near-time data storage facility

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07. Hive

A tool for handling structured data kept in a Hadoop Distributed File System is Apache Hive. It runs on Apache Hadoop and simplifies querying and analysis. For users who are familiar with SQL, it provides an interface for querying data that is similar to SQL (HiveQL).

Features: 

• Hive Query Language allows several users to query the data simultaneously
• Apache Hive entirely fulfills Apache Hadoop’s low-level interface requirement
• Integration with tools from the Hadoop ecosystem

Uses:

• Information warehousing
• Quick data analysis
• Managing, writing, and accessing massive files stored in dispersed storage

Benefits: 

• With its Gantt charts, it facilitates the maintenance of precise project timetables and increases productivity through task automation.
• It is a cheaper option.
• In addition to analyzing data, it gives users the ability to read and write data in a systematic way.

Project examples:

• Real-time queries and analytics
• Data processing and transformation

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Big Data is the cornerstone of artificial intelligence, machine learning, and analytics in data science. It is essential for making wise judgments and promoting corporate expansion. It assists businesses in streamlining their processes, cutting expenses, and increasing productivity.

To become an expert in data science or business analytics, consider opting for an online MSC data science or business analytics from the Manipal Academy of Higher Education. The curriculum gives you the ideal balance of machine learning, big data analytics, and statistics to help you become proficient in using real-world data to solve issues. With MAHE’s effective learning pedagogy, world-class faculty, advanced digital learning platform, and more, students can unleash their potential and open a gateway to a flourishing career in data science.

Conclusion

Big data in data science is the key to releasing the potential of data and transforming it into insights that can be put to use for future growth. To put data science into action, with the use of data science open-source tools, organizations may fully utilize their data, fostering innovation and evidence-based decision-making. These solutions give users the ability to find new opportunities, optimize operations, detect patterns, and gain insights from huge databases. 

These free and effective big data tools for data science can help analysts in extracting valuable insights from the data. These tools, each possessing unique characteristics and functions, are essential components of open-source big data solutions. You can learn all these tools and more with MAHE and steer towards a rewarding career.