Best Big Data Tools and Technologies – The world of Big Data is just getting bigger. Businesses of all types generate vast amounts of data year after year and find more and more ways to use it to improve operations, better understand customers, deliver products faster, reduce costs, and more.

Additionally, executives who want to get the most out of data faster are looking for real-time analytics. All of this stimulates significant investments in tools and technologies for working with big data.

In its August 2021 report, market research firm IDC estimated expected global spending on big data tools and analytics systems at $215.7 billion in 2021, up 10.1% year-on-year. It also predicts that spending will grow 12.8% per year through 2025.

10 Best Big Data Tools and Technologies

Best Big Data Tools and Technologies That You Sould Know & Use

The list of big data tools and technologies is long: there are many commercial products that help companies implement the full range of data-driven analytics initiatives, from real-time reporting to machine learning applications.

In addition, there are many open source big data tools, some of which are also offered commercially or as part of big data platforms and managed services.

Below is an overview of 10 popular open source tools and technologies for big data management and analysis, listed in alphabetical order with a brief description of their key features and capabilities.

1. Trino

As mentioned above, Trino is one of two branches of the Presto request processing system. Trino, known as PrestoSQL until it was rebranded in December 2020, “runs at a ridiculous pace,” according to the Trino Software Foundation.

This group, which oversees the development of Trino, was originally founded in 2019 as the Presto Software Foundation; Her name was also changed as part of the rebranding.

Trino enables users to query information no matter where it’s stored, with support for native queries in Hadoop and other data stores. Like Presto also Trino:

designed for both situational interactive analysis and long-running batch queries;
can combine data from multiple systems into queries; And
works with Tableau, Power BI, R and other BI and analytics tools.

2. Storm

Another open-source Apache technology, Storm is a real-time distributed computing system designed to reliably process unbounded streams of data.

According to the project website, it can be used for applications involving real-time analytics, online machine learning, and continuous computing, as well as data extraction, transformation, and loading (ETL) jobs.

Storm clusters are similar to Hadoop clusters, but applications continue to run unless stopped. The system is fault-tolerant and guarantees that the data will be processed.

Additionally, the Apache Storm website says it can be used with any programming language, message queuing system, and database. Storm also includes the following items:

the Storm SQL feature, which allows you to run SQL queries against streaming datasets;
Trident and Streams API, two other high-level processing interfaces in Storm; And
Using Apache Zookeeper technology to coordinate clusters.

3. Spark

Spark is an in-memory data science engine that can run on clusters managed by Hadoop YARN, Mesos, and Kubernetes, or standalone.

It allows you to perform large-scale transformations and data analysis; can be used for batch and streaming applications as well as machine learning and graph processing. All of this is supported by the following built-in modules and libraries:

Spark SQL for streamlined processing of structured data using SQL queries;
Spark Streaming and Structured Streaming, two stream processing engines;
MLlib, a machine learning library with algorithms and related tools; And
GraphX, an API that adds support for graphics applications.

Information can be accessed from a variety of sources, including HDFS, relational and NoSQL databases, and flat file datasets. Spark also supports various file formats and offers a variety of APIs for developers.

But its most important calling card is speed: Spark developers claim that it can run 100x faster than its traditional MapReduce counterpart when processing it in memory for batch jobs.

As a result, Spark has become the go-to choice for many batch applications in big data environments, and has also acted as a general-purpose engine. Originally developed at UC Berkeley and currently maintained by Apache, it can also handle data on disk when datasets are too large to fit in available memory.

4. Samza

Samza is a distributed stream processing system developed by LinkedIn and is currently an open source project supported by Apache. According to the project’s website, Samza allows users to build stateful applications that can process data from Kafka, HDFS, and other sources in real time.

The system can run on Hadoop YARN or Kubernetes, and a standalone deployment option is also offered. Samza’s website states that it can process “several terabytes” of data status information with low latency and high throughput for rapid analysis.

When running batch applications, the unified API can also use the same code written to work with streaming data. Other features include the following:

built-in integration with Hadoop, Kafka and some other data platforms;
the ability to run as a built-in library in Java and Scala applications; And
fault-tolerant features for rapid recovery from system failures.

5. Presto

Formerly known as PrestoDB, this open-source SQL query engine can simultaneously process both fast queries and large amounts of information across distributed datasets. Presto is optimized for low-latency interactive queries and scales to support multi-petabyte analytics applications in data warehouses and other repositories.

The development of Presto started in 2012 at Facebook. When its developers left the company in 2018, the technology split into two branches: PrestoDB, still owned by Facebook, and PrestoSQL, introduced by the original developers.

This lasted until December 2020 when PrestoSQL was renamed to Trino and PrestoDB reverted to the Presto name. The open-source Presto project is currently curated by the Presto Foundation, which was established in 2019 as part of the Linux Foundation.

Presto also includes the following features:

Support for querying data in Hive, various databases and proprietary information stores;
the ability to combine data from multiple sources into a single query; And
Query response time, which typically ranges from less than a second to several minutes.

6. Kylin

Kylin is a distributed information warehouse and big data analytics platform. It provides an OLAP (Analytic Information Processing) engine designed to work with very large datasets.

Because Kylin is built on top of other Apache technologies, including Hadoop, Hive, Parquet, and Spark, proponents say it can easily scale to handle large amounts of data.

In addition, it works quickly and provides responses to requests measured in milliseconds. In addition, Kylin has a simple interface for multidimensional big data analysis and integrates with Tableau, Microsoft Power BI and other BI tools.

Kylin was originally developed by eBay, which made it available as open-source technology in 2014; the next year it became a top-level project within Apache. Other features it offers include:

ANSI-SQL interface for multidimensional big data analysis;
Integration with Tableau, Microsoft Power BI and other BI tools; And
Precalculation of multidimensional OLAP cubes to speed up analysis.

7. Kafka

Kafka is a distributed event streaming platform used by more than 80% of the Fortune 100 and thousands of other organizations for high-performance data pipelines, streaming analytics, data integration, and mission-critical applications, according to Apache.

Simply put, Kafka is a framework for storing, reading, and analyzing streaming data. Technology separates information flow and systems, keeps the data flow in order to use it elsewhere.

It works in a distributed environment and uses the high-performance TCP network protocol to communicate with systems and applications. Kafka was developed by LinkedIn and acquired by Apache in 2011.

Some of the key components of Kafka are listed below:

a set of five core APIs for Java and the Scala programming language;
Fault tolerance for servers and clients in Kafka clusters; And
elastic scalability up to 1,000 “brokers” or storage servers per cluster.

8. Airflow

Airflow is a workflow management platform for planning and executing 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 correct order and has access to the required system resources.

Airflow is also touted as being easy to use: workflows are built in the Python programming language and can be used to build models for machine learning, data transfer, and various other purposes.

The platform appeared on Airbnb in late 2014 and was officially announced as open-source technology in mid-2015; The following year, it joined the Apache Software Foundation’s incubator program and became a top-level Apache project in 2019. Airflow also includes the following key features:

modular and scalable architecture based on the concept of Directed Acyclic Graphs (DAGs) that illustrate dependencies between different tasks in workflows;

Web application user interface for visualizing data pipelines, monitoring their production status and troubleshooting; And
pre-built integrations with major cloud platforms and other third-party services.

9. Delta Lake

Databricks Inc., a software vendor founded by the developers of the Spark processing engine, developed Delta Lake and then made the Spark-based technology available through the Linux Foundation in 2019.

The company describes Delta Lake as “an open-format storage tier that provides the reliability, security, and performance of your data lake for streaming and batch operations.”

Delta Lake is not a replacement for data lakes; Rather, it’s designed to sit on top of them and create a single repository for structured, semi-structured, and unstructured data, breaking down their isolation that big data tools can disrupt.

Additionally, according to Databricks, using Delta Lake can help prevent data corruption, speed up queries, improve data freshness, and aid in compliance efforts. technology too

supports ACID transactions;
saves data in the open Apache Parquet format; And
contains a set of Spark-compatible APIs.

10. Drill

The website describes Apache Drill as “a low-latency, distributed query engine for large datasets, including structured and semi-structured/nested data.” Drill scales to thousands of cluster nodes and is capable of querying petabytes of data using SQL and standard connection APIs.

Designed for exploring big data toolsets, drill is layered on multiple data sources, allowing users to query a wide range of information in a variety of formats, from Hadoop sequence files and server logs to NoSQL databases and cloud object storage . It can also do the following:

Access to most relational databases via a plugin;
work with widely used BI tools such as Tableau and Qlik; And
can run in any distributed cluster environment, but requires Apache’s ZooKeeper software to manage cluster information.

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