The ever-increasing volume and variety of data is driving companies to invest more in big data tools and technologies. This trend is creating a high demand for skilled professionals who can analyse and interpret large datasets, turning them into actionable insights that can drive business decisions.
For professionals looking to advance their careers in the data and analytics space, now is an opportune time to upskill and specialise in big data technologies. Companies are seeking individuals who are proficient in programming languages such as Python and R, as well as experienced in using big data tools such as Hadoop, Spark, and SQL.
Additionally, the ability to effectively communicate and present complex data findings is becoming increasingly valuable for data scientists and analysts. Developing these skillsets can open up a plethora of career opportunities in a fast-growing industry.
We share 10 popular open source tools and technologies for managing and analysing big data, listed in alphabetical order with a summary of their key features and capabilities provided by TechTarget.
1. Airflow
Airflow is a workflow management platform for scheduling and running complex data pipelines in big data systems. It enables data engineers and other users to ensure that each task in a workflow is executed in the designated order and has access to the required system resources.
2. Delta Lake
Databricks Inc., a software vendor founded by the creators of the Spark processing engine, developed Delta Lake, and then open sourced the Spark-based technology in 2019 through the Linux Foundation. The company describes Delta Lake as "an open format storage layer that delivers reliability, security and performance on your data lake for both streaming and batch operations."
3. Drill
The Apache Drill website describes it as "a low latency distributed query engine for large-scale datasets, including structured and semi-structured/nested data." Drill can scale across thousands of cluster nodes and is capable of querying petabytes of data by using SQL and standard connectivity APIs.
4. Druid
Druid is a real-time analytics database that delivers low latency for queries, high concurrency, multi-tenant capabilities and instant visibility into streaming data. Multiple end users can query the data stored in Druid at the same time with no impact on performance, according to its proponents.
5. Flink
Another Apache open source technology, Flink is a stream processing framework for distributed, high-performing and always-available applications. It supports stateful computations over both bounded and unbounded data streams and can be used for batch, graph and iterative processing.
6. Hadoop
A distributed framework for storing data and running applications on clusters of commodity hardware, Hadoop was developed as a pioneering big data technology to help handle the growing volumes of structured, unstructured and semistructured data. First released in 2006, it was almost synonymous with big data early on; it has since been partially eclipsed by other technologies but is still widely used.
7. Hive
Hive is SQL-based data warehouse infrastructure software for reading, writing and managing large data sets in distributed storage environments. It was created by Facebook but then open sourced to Apache, which continues to develop and maintain the technology.
8. HPCC Systems
HPCC Systems is a big data processing platform developed by LexisNexis before being open sourced in 2011. True to its full name -- High-Performance Computing Cluster Systems -- the technology is, at its core, a cluster of computers built from commodity hardware to process, manage and deliver big data.
9. Hudi
Hudi (pronounced hoodie) is short for Hadoop Upserts Deletes and Incrementals. Another open source technology maintained by Apache, it's used to manage the ingestion and storage of large analytics data sets on Hadoop-compatible file systems, including HDFS and cloud object storage services.
10. Iceberg
Iceberg is an open table format used to manage data in data lakes, which it does partly by tracking individual data files in tables rather than by tracking directories. Created by Netflix for use with the company's petabyte-sized tables, Iceberg is now an Apache project. According to the project's website, Iceberg typically "is used in production where a single table can contain tens of petabytes of data."
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