Best Big Data Tools and Technologies – The world of big data is only getting bigger. Organizations of all stripes generate massive amounts of data year after year, and are finding more and more ways to use it to improve operations, better understand customers, deliver products faster and lower costs, and more.

In addition, business leaders looking to get the most out of data faster are looking for real-time analytics. All this stimulates significant investments in tools and technologies for working with big data.

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

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Best Big Data Tools and Technologies

The list of big data tools and technologies is long: there are many commercial products available to help organizations 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 17 popular open source tools and technologies for managing and analyzing big data, listed in alphabetical order with a brief description of their key features and capabilities.

1. Air flow

Airflow is a workflow management platform for planning and running complex data pipelines in big data systems. It allows data engineers and other users to ensure that each task in a workflow runs in the correct order and has access to the necessary 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 machine learning models, data transfer, and various other purposes.

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

modular and scalable architecture built 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
ready-made integrations with major cloud platforms and other third-party services.

2 Delta Lake

Databricks Inc., a software vendor founded by the creators 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 layer 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 is designed to sit on top of them and create a single repository for structured, semi-structured, and unstructured data, thereby removing their isolation that can interfere with big data tools.

Additionally, using Delta Lake can help prevent data corruption, speed up queries, improve data freshness, and support compliance efforts, according to Databricks. Technology also

supports ACID transactions;
stores data in the open Apache Parquet format; And
includes a set of Spark compatible APIs.

3. Drill

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

Designed to explore big data tools sets, Drill is layered on top of 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 most relational databases through a plugin;
work with widely used BI tools such as Tableau and Qlik; And
run in any distributed cluster environment, though it requires Apache’s ZooKeeper software to maintain cluster information.

4. Druid

Druid is a real-time analytics database that provides low query latency, high concurrency, multi-user capabilities, and instant visibility into streaming data. According to its proponents, multiple end users can simultaneously request data stored in Druid without any performance impact.

Written in the Java language and created in 2011, Druid became an Apache technology in 2018. It is generally considered a high-performance alternative to traditional information stores and is best suited for event-driven data.

Like the data warehouse, it uses a column-oriented storage solution and can load files in batches. However, it also uses features from search engines and time series databases, including the following:

native inverted search indexes to speed up searching and filtering data;
separating data by time and generating queries; And
flexible schemas with native support for semi-structured and nested data.

5. Flash

Another open source Apache technology, Flink is a stream processing framework for distributed, high performance, and always available applications. It supports stateful computing over bounded and unbounded data streams and can be used for batch, graph and iterative processing.

One of the main advantages of Flink, which fans of this technology talk about, is its speed: it can process millions of events in real time at low latency and high throughput. Designed to work in all common cluster environments, Flink also includes the following features:

computing in memory with the ability to access disk storage if necessary;
three levels of API for creating different types of applications; And
a set of libraries for complex event handling, machine learning, and other common big data use cases.

6. Hadoop

A distributed environment for storing data and running applications on clusters of standard hardware (commodity hardware), (the so-called method of mass cluster computing).

Hadoop was developed as an advanced big data tools and technology for handling the growing volume of structured, unstructured and semi-structured information. First released in 2006, it has become almost synonymous with big data; it has since been partly eclipsed by other technologies, but is still widely used.

Hadoop has four main components:

Hadoop Distributed File System (HDFS), which partitions information into blocks for storage on cluster nodes, uses replication techniques to prevent data loss and manages access to them;
YARN, short for Yet Another Resource Negotiator, which schedules jobs on cluster nodes and allocates system resources for them;
Hadoop MapReduce, a built-in batch processing engine that splits large amounts of computation and runs it on different nodes for speed and load balancing; And
Hadoop Common, a common set of utilities and libraries.

Initially, Hadoop was focused only on running MapReduce batch applications. The addition of YARN in 2013 opened it up to other processing engines and use cases, but the framework is still closely tied to MapReduce.

The broader Apache Hadoop ecosystem also includes various tools and additional frameworks for processing, managing and analyzing big data.

7. Hive

Hive is SQL-based data warehouse infrastructure software for reading, writing, and managing large datasets in distributed storage environments. It was created by Facebook, but then became open source to Apache, which continues to develop and maintain this technology.

Hive runs on top of Hadoop and is used to process structured information; more specifically, it is used for summarizing and analyzing data, as well as for querying large amounts of data.

While it cannot be used to process online transactions, real-time updates, queries, or jobs that require low-latency data, the developers describe Hive as scalable, fast, and flexible.

Other key features include the following:

standard SQL functionality for queries and data analytics;
a built-in mechanism to help users impose a structure on various data formats; And
access to HDFS files and files stored on other systems such as the Apache HBase database.

8. HPCC Systems

HPCC Systems is a big data processing platform developed by LexisNexis and made open source in 2011. In accordance with its full name – High-Performance Computing Cluster – the technology is essentially a cluster of computers created on the basis of standard hardware for processing, managing and delivering big data.

A production-ready data lake platform that enables rapid data development and exploration, HPCC Systems includes three main components:

Thor, a data processing engine that is used to clean, combine, and transform data, as well as profiling, analyzing, and preparing it for use in queries;
Roxie, a data delivery mechanism used to provide prepared information from the cleaning system; And
Enterprise Control Language (ECL), a programming language for application development.

9. Hudi

Hudi (pronounced “hoodie”) is short for Hadoop Upserts Deletes and Incrementals. Another open source technology backed by Apache is used to manage the capture and storage of large analytical datasets on Hadoop compatible file systems, including HDFS and cloud object storage services.

First developed by Uber, Hudi is designed to provide efficient, low-latency data entry and preparation. What’s more, it includes a data governance framework that organizations can use to:

simplify incremental data processing and data pipeline development;
improve the quality of information in big data systems ; And
manage the life cycle of datasets.

10. Iceberg

Iceberg is an open table format used to manage data in lakes, achieved in part by keeping track of individual files of information in tables rather than directories.

Created by Netflix for use with their petabyte spreadsheets, Iceberg is now an Apache project. According to the site, Iceberg is typically “used in production, where a single table can contain tens of petabytes of data.”

Designed to improve upon the standard schemas found in tools such as Hive, Presto, Spark, and Trino, the Iceberg format table has functions similar to SQL in relational databases. However, it is also adapted to use multiple mechanisms working with the same dataset. Other notable features include the following:

schema evolution to modify tables without the need to rewrite or migrate data;
implicit data partitioning, which eliminates the need for users to maintain partitions; And
a “time travel” feature that supports reproducible queries using the same table snapshot.

11. Kafka

Kafka is a distributed event streaming platform that, according to Apache, is 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.

Simply put, Kafka is a framework for storing, reading, and analyzing streaming data. Technology separates the flow of information and systems, holding the flow of data in order to then use it in other places.

It operates in a distributed environment and uses the high performance TCP network protocol to communicate with systems and applications. Kafka was created by LinkedIn and was taken over 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.

12. Kylin

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

Because Kylin is built on top of other Apache technologies, including Hadoop, Hive, Parquet, and Spark, it can be easily scaled up to process large amounts of data, according to proponents.

In addition, it works quickly, providing responses to requests measured in milliseconds. Also, 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 an open source technology in 2014; the next year it became a top-level project within Apache. Other features it provides include:

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

13. 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.

Development of Presto began at Facebook in 2012. When its creators left the company in 2018, the technology split into two branches: PrestoDB, which was still led by Facebook, and PrestoSQL, which was launched by the original developers.

This continued 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 created as part of the Linux Foundation in 2019.

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 in a single query; And
query response time, which typically ranges from less than a second to several minutes.

14. Samza

Samza is a distributed stream processing system created by LinkedIn and is currently an open source project powered 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 top of Hadoop YARN or Kubernetes, and a standalone deployment option is also offered. Samza’s website says it can process “several terabytes” of data state information at low latency and high throughput for fast analysis.

When running batch applications, the unified API can also use the same code that was 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 designed to quickly recover from system failures.

15. 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 both batch and streaming applications, as well as machine learning and graph processing in. All this is supported by the following set of built-in modules and libraries:

Spark SQL, for optimized processing of structured data using SQL queries;
Spark Streaming and Structured Streaming, two stream processing modules;
MLlib, a machine learning library including algorithms and related tools; And
GraphX, an API that adds support for graph 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 rich set of APIs for developers.

But its main calling card is speed: the Spark developers claim that it can run 100 times faster than the traditional MapReduce counterpart on batch jobs when processed in memory.

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

16.Storm

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

According to the project website, it can be used for applications that include 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 continuously unless they are stopped. The system is fault tolerant and guarantees that the data will be processed.

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

the Storm SQL function, which allows you to execute 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.

17. Trino

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

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

Trino allows users to query information regardless of where it is stored, with support for native querying in Hadoop and other data repositories. Like Presto, Trino also:

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

Also Consider: NoSQL Databases

NoSQL databases are another major element of big data technology. They differ from traditional SQL-based relational databases in that they support flexible schemas.

This makes them suitable for working with huge amounts of all types of information – especially unstructured and semi-structured data, which are poorly suited to the strict schemas used in relational systems.

NoSQL software emerged in the late 2000s to help deal with the growing amount of information that organizations were generating, collecting and analyzing as part of big data initiatives.

Since then, NoSQL databases have become widespread and are now used by enterprises in various industries. Many are open source technologies that are also commercially available from vendors, and some are proprietary products controlled by a single vendor.

In addition, NoSQL databases themselves come in many different types that support different big data tools. Here are the four main categories of NoSQL, with examples of available technologies in each:

Document databases. They store data elements in document-like structures using formats such as JSON. Examples include Apache CouchDB, Couchbase Server, MarkLogic, and MongoDB.
Graph databases. They connect data “nodes” into graph-like structures to highlight the relationships between information items. Examples: AllegroGraph, Amazon Neptune and Neo4j.
Key-value stores. They combine unique keys and their associated values into a relatively simple data model that scales easily. Aerospike, Amazon DynamoDB, and Redis are examples.
Multi-column databases. They store information in tables that can contain very many columns to handle a huge amount of data items. Examples are Cassandra, Google Cloud Bigtable and HBase.

Multi-model databases have also been created with support for various NoSQL approaches, and in some cases SQL; examples are ArangoDB and Azure Cosmos DB from Microsoft.

Other NoSQL vendors have added multi-model support to their databases. For example, MarkLogic now includes a graph store, Couchbase Server supports key-value pairs, and Redis offers document and graph database modules.

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