Discover the 14 essential Big Data tools and technologies shaping 2024
It's 2024, the era of unabating digital transformation, where employing big data tools is not just an imperative but a linchpin of technological innovation. Big data tools equip forward-thinking enterprises with strategic advantages and empower them to navigate through the expansive data landscape and derive competitive insights. To attain resilience, agility and scalability in the face of data deluge, it's crucial that businesses harness the power of big data tools. Let's explore the top 14 big data tools that will shape your innovative 2024 and catapult your enterprise to an unparalleled success and heights of innovation.
On to transformative journey with top 14 Big data tools
1. Hadoop: Apache Hadoop is an open-source framework, designed with the power to scale up to thousands of machines from single servers with separate local computation and storage. This software does not rely on hardware for delivering high-availability. Hadoop uses simple programming models and allows distributed processing of enormous data sets by clustering multiple computers. It can process structured, semi-structured and unstructured data. The library can detect and handle failures at the application layer, thus delivering high-availability. This is the best tool if you are looking for processing large-scale data. Hadoop is characterised by three components:
- HDFS : serves as a storage layer
- YARN: serves as the task scheduling and resource management layer
- MapReduce: serves ad the processing layer
2. Spark: Apache Spark is another open-source tool with the potential of fast data processing. It is a distributed computing system ideal for processing big data and analytics. It is a multilingual tool capable of executing both batch and real-time analytics. Spark has a powerful set of library consisting of SparkSQL, MLLib, GraphX, and Spark Streaming. This tool uses in memory caches and requires RAM. This is the best tool for real-time analytics.
3. Kafka: Kafka is another open-source graphical user interface and a distributed event streaming platform. Kafka stores data in a durable, distributed fault-tolerant cluster and renders high-performance data pipelines, event streaming, streaming analytics, data integration and storage. It is powered with the potential to read, write, and process streams of events across a multiple range of programming languages. It can connect to several separate clusters, rendering high availability.
4. SQL Databases: This relational database is best for storing and processing information in tabular format, with separate rows and columns constituting varied data attributes and the diverse relationships between the data values. SQL databases, short for Structured Query Language, can be ideally employed for maintaining and optimizing database performance. This tool can help your enterprise optimize performance with its array of value-adding features and facilitate ease and speed in database operations. It's easy to maintain with its built-in automation tools that help SQL database to control, repair and maintain.
5. HIVE: This data warehousing tool based on SQL is an excellent tool for extracting insightful data and valuable information. This tool performs the task of storing data in all forms generated from various sources at a single location. It is known for its speed and efficient scaling of big data tool and ability to query Petabytes (PB). It allows multiple users to query the data simultaneously and offers support for ETL (Extract Transform and Load).
6. Pig: Apache Pig is another tool that makes analysing of high-level language easy. Pig is a platform that allows for analysing large data sets comprising high-level language and evaluating the analytics. The tool allows users to develop their own functions for processing special-purpose data. The programs of PIG platform has structure that allows for substantial parallelization that equips them to handle huge data sets.
7. Flink: This is also one of the highly efficient open-source data analytics technology that also streams processing framework. It is developed to run across all common cluster and is a distributed, constantly-available data streaming powered by high-performance. It is characterised by accurate results for all data types and is fault-tolerant. This is an ideal tech if you are looking for a tool for large scale that can run on multiple nodes and process terabytes of data.
8. HBase: This is your go-to tool when in need of random and real-time read/write access to your data. It is a distributed and scalable database that can also store big data. This tool is however Non-relational database modelled after Bigtable and performs similar to that of Bigtable. It has modular and linear scalability.
9. Presto: This is another one of the reliable open-source SQL query engine. Presto is also known for its speed and efficiency for running interactive/ad hoc queries for high-volume applications. It is capable of operating at massive scale like that of Meta or even run for just a few nodes. This tool can query relational as well as NoSQL databases, data lakes, data warehouses and several other connectors prominent today. Data from multiple sources across your organization can be sourced in Presto for allowing efficient analytics.
10. ElasticSearch: This is your go-to tool if you are seeking for solution at scale and in real-time. ElasticSearch gives relevant results by accelerating root cause analysis and giving results in unprecedented speed. It uses unified solution with EDR, Cloud Security and SIEM and automate investigation, automaton and response at scale. This tool enables businesses to provide search applications powered by AI and ML for their clients and employees.
11. Splunk: This is a cloud platform with easy and straightforward dashboard that allows for data search, analytics and visualization in a seamless manner. Enterprises can easily perform their streaming, collecting and indexing of data at any scale. The Splunkbase allows enterprises to customize their unique business requirements free and extract transparent and actionable insights from raw data.
12. Tableau: Tableau emerged and remained as one of the most efficient business intelligence and data visualization tool. Enterprises have been empowered with an efficient method to organize, manage, visualize and understand their data easily. Users can easily collaborate and share data in Tableau through the visualization sheet, dashboards, etc. Its users access authentication and permission systems effect strong security.
13. PowerBI: The best tool for in-depth analysis. PowerBI allows for easy connection of data and seamless infusion of visuals into apps. With PowerBI users can connect the diverse data sources in the same ecosystem and develop a single source to extract value from all your data. Users are empowered with the potential of bridging the gap between decisions and insights through an unparalleled interoperation.
14. NoSQL Databases: This non-tabular database is another top Big data tool that each enterprise must prioritize. By employing a variety of data models, NoSQL databases access and manage the data to optimize applications, particularly for apps that demand flexible and large data models and low latency. The NoSQL databases store data different from relational databases.
Those are the top Big Data Models that will equip you and your enterprise with competitive prowess and sustained growth. Whether you are hustling for amplified operational efficiencies or attaining unparalleled foresights of the market trends, these tools will work as your anchor in your sail of technological advancements.
Alok K Singh
Co-founder, SNVA Group