Top 21 Data Engineering Tools: Big Data Tools

In this blog, we will explore the top Big Data and Engineering Tools that are highly used for Data Engineering work to handle large quantities of data.

In the last few years, big data has shifted from an early-adopter technology to an expectation that many modern applications are built on top of these days.

As more companies enter this space, it’s becoming increasingly important to think about the tools that these data engineers use to build and maintain their applications.

To that end, we’ve partnered with SemaphoreCI to learn about how different data engineering teams are thinking about their roles in the future of big data at mid-sized tech companies, and how they’re thinking about their workflows as well as their various toolsets to accomplish those tasks.

Related Article: Who is a Data Engineer & How to Become a Data Engineer?

Big Data Engineering Tools

Here are the few top big data engineering tools that are highly used for big data processing in data engineering work.

Related Article: Top 14 tools for Data Engineering

1) Databricks

Databricks is a big data platform focused on interactive analytics. Databricks has two core capabilities: cloud-based Spark and a serverless platform that brings Spark to users’ laptops, desktops, and mobile devices.

The company was founded in 2013 by some of the original creators of Apache Spark.

The company is based in San Francisco and backed by Andreessen Horowitz, New Enterprise Associates (NEA), Ignition Partners, Accel Partners, Kleiner Perkins Caufield & Byers (KPCB), Index Ventures, Meritech Capital Partners, and other investors.

2) Amazon Redshift

Amazon Redshift is a superfast data warehouse that’s quick, all-encompassing, and not expensive to use for analyzing all of your data using a standard SQL query.

With Amazon Redshift, you can load your data using standard SQL (SELECT, INSERT, UPDATE, and DELETE statements), and query using a familiar SQL interface or by using AWS Glue Data Catalog.

It supports Hive for ad hoc queries and complex analysis, Spark for interactive analysis of large datasets, and Presto for interactive querying of large data sets.

It also supports most other popular open-source analytical engines such as Python, R, Julia, Scala, and more.

The architecture of Amazon Redshift enables customers to scale their data warehouses quickly without provisioning hardware capacity in advance.

3) Presto

Hadoop’s popularity is well-documented, but it’s by no means alone in being a Big Data platform.

It comes as no surprise then that Presto—the interactive SQL engine for Big Data—is a close second to Hive, which is another popular option for querying Hadoop.

Presto has been developed by Facebook and offers great performance and scale with fewer resources than other Big Data engines.

At its core, Presto is a distributed execution engine that supports high-level declarative queries expressed in SQL.

On top of that infrastructure, it offers additional capabilities including JIT (just-in-time) compilation, hybrid execution plans based on cost analysis, and more.

4) Spark SQL

Spark SQL is a Spark module that enables users to load, query, and analyze large-scale datasets.

It also allows direct access to any source of structured or semi-structured data from within other programming languages (e.g., Python, Java) by providing an API for reading/writing directly from/to files in supported formats such as Parquet, ORC, Avro, JSON, and others.

Spark SQL makes it easy to create complex queries with minimal code, simplifying big data processing tasks.

Spark SQL is designed for both batch and interactive use cases, making it easy to build applications that process massive amounts of data in real-time.

5) Looker

Looking at how to process, understand and visualize your company’s data? Looker has simplified Big Data analysis and offers a visual interface for interacting with complicated datasets.

As a powerful tool, Looker is used by over 100 companies and is one of those Big Data Engineering Tools which are always highly recommended by industry leaders.

It provides user-friendly analytics tools that scale from a single dashboard or report to an enterprise-wide architecture.

6) Cloudera

Cloudera is an enterprise software company that offers software for processing, analyzing, and distributing big data.

Their flagship product is Cloudera Enterprise, a platform designed to help you make sense of your company’s structured and unstructured information.

Cloudera also offer solutions for real-time analytics and business intelligence.

Cloudera has been around since 2008 and was recently acquired by Intel in 2018. The company has raised $926 million in funding from investors like Accel Partners, Greylock Partners, In-Q-Tel (the investment arm of the U.S. Central Intelligence Agency), T Rowe Price Group Inc., Tiger Global Management LLC, and others.

7) Google Cloud Dataflow

Google Cloud Dataflow is a fully managed service for building and executing data processing pipelines.

Dataflow pipelines are made up of individual components that perform specific functions such as loading data, transforming it, and sending it to storage.

Using a simple programming model, you can orchestrate all of these elements into a single pipeline.

The service handles everything from resource management to task scheduling to monitoring execution so you don’t have to.

Best of all, there’s no need for complex upfront planning or changes in your code; simply write your code once and run it anywhere on-premises or in any public cloud.

8) Apache Drill

Apache Drill is an open-source distributed SQL query engine that supports joins and large-scale parallelism.

It enables users to run interactive queries directly against a variety of databases including Hadoop, HDFS, and MongoDB as well as conventional RDBMS.

Drill lets you avoid vendor lock-in while allowing you to leverage modern big data systems.

The project was announced in February 2013 by MapR Technologies, which is now part of IBM.

The Drill community has grown significantly since then with contributors from Cloudera, Hortonworks, Microsoft Azure Data Engineering Team, and others contributing to its development.

Drill uses ANSI SQL standard for syntax but can also be extended using user-defined functions (UDF).

UDF allows developers to write Java code or Python code in order to extend its functionality beyond what’s provided by ANSI SQL.

9) Big Query

BigQuery is Google’s fully managed, petabyte-scale, low-cost analytics data warehouse. BigQuery enables SQL-like queries against multi-terabyte datasets running on Google’s infrastructure.

It is easy to set up and use and offers pay-as-you-go pricing. BigQuery is accessible through a REST API, as well as from client libraries for Python, Java, Go, Ruby, PHP and Node.js.

10) Stitch tool

Hadoop is a big ecosystem with lots of different components and toolkits. While each has its own purpose, it can be difficult to know what you should choose for your specific use case.

The Stitch Data Infrastructure is an attempt to help you sort through all of these options by highlighting how various team members at some of today’s most innovative companies think about their usage and needs.

From engineers to CTOs, we spoke with over 150 data engineers from mid-sized tech companies across industries like finance, e-commerce, travel, and more.

11) Flink

Founded in 2013, Flink is a fast, efficient, and resilient streaming platform that has become one of today’s most popular big data open-source projects.

It allows the processing of massive amounts of unstructured data such as clickstreams, social media feeds, or IoT sensor outputs at unparalleled speed and scale.

The core of Flink is written in Java and runs on YARN (Yet Another Resource Negotiator) with an optional native code extension for performance-critical parts.

The interface offers APIs for Java, Scala, Python, and R, which can be used to develop custom applications on top of it.

12) Apex

Apex is a SQL-like language that you can use to query Hadoop and Hive tables. As a replacement for Hive, Apex is designed to be faster and more convenient for developers and database administrators who are used to working with traditional relational databases.

Because it’s not a full programming language, Apex does have some limitations. But it also has some useful features that might make it easier for your team to work with big data than in other languages.

13) Airflow

Facebook, Airbnb, and Spotify are open-sourcing Airflow, a platform for programmatically authoring, scheduling, and monitoring workflows.

It can be used to design and operate large-scale workflows in different environments (such as those spanning on-premise infrastructure and cloud services) with optimal efficiency.

The library has already been widely adopted by data scientists because of its easy-to-use interface.

14) Kafka

No surprise that Kafka made it onto everyone’s list of top big data engineering tools.

Whether you are an enterprise or a scrappy startup, if you have hundreds of thousands of users, chances are you have your hands in Kafka.

That being said, when it comes to starting out with Apache Kafka, many people do not know where to begin.

The first step is understanding what makes Kafka tick and why it is such a powerful tool for processing data at scale.

15) Hadoop

Hadoop is one of those big data engineering tools that has managed to become synonymous with big data and data analytics.

It allows for large amounts of raw data to be collected and processed, but it’s not exactly intuitive, nor is it easy to use.

It provides an underlying framework for people to run any number of tasks at once—from batch processing all the way through real-time analytics—and make sense of extremely complex datasets.

16) dbt

The most commonly used database in tech, dbt (pronounced dee-bee-tee) is an open-source distributed database management system.

This makes it incredibly flexible and easy to use for companies of all sizes.

dbt allows for both storing and processing of information from one central location, with a user interface similar to desktop applications.

It’s also extremely secure so that only those who need access can see your data. For many companies, dbt has become a core part of their infrastructure and it’s why we included it on our list of top big data engineering tools.

17) Cassandra

Cassandra is a distributed database for managing large amounts of structured data across many commodity servers, providing high availability with no single point of failure.

It’s often used in big-data applications that require constant access to a central repository of information.

In terms of functionality, Cassandra is similar to Amazon’s DynamoDB, which is considered NoSQL.

Cassandra was created by Facebook and released as an open-source project in 2008. It can be run on either Hadoop or Mesos clusters and supports various languages including Java, C++, and Python.

18) Redis

Redis is open source software providing an in-memory database, used as a database server.

Redis has data structures and commands similar to a NoSQL database, while also supporting interactive and dynamic capabilities with on-disk persistence.

It is worth mentioning that Redis is one of many open-source projects from 10gen Inc., which was acquired by Oracle in October 2014.

Redis can be deployed on every major operating system, including Linux, OS X, Windows, Solaris, and FreeBSD.

However, it can be difficult to install correctly because most distributions don’t package it or have outdated packages available.

19) Snowflake

For years, companies that needed their databases to perform at a massive scale have had to turn to third-party systems or build complex systems in-house.

But now Google’s cloud-based data warehouse service which lets companies run queries on up to 5 TB of data, across 12 trillion cells is starting to gain traction in Silicon Valley.

I think it’s going to become more and more popular, says Matt Asay, vice president of business development and marketing at Snowflake Computing.

People are realizing that there is no need for them to buy an appliance from Oracle or Teradata when they can get essentially similar functionality for free.

20) Apache Hive

Hive is a data warehouse infrastructure built on top of Hadoop for managing large datasets.

Hive provides a SQL-like interface for ad hoc queries, coupled with mechanisms for automatic query optimization and execution.

It also supports insert/update/delete operations, as well as user-defined functions written in Java, Python, or Ruby.

21) PowerBI

One of Microsoft’s flagship products, PowerBI allows for easy data visualizations and analysis.

PowerBI is used by many large companies to analyze massive amounts of user activity and keep track of what’s going on with their customers at all times.

You can connect it to your SQL database or other sources such as Salesforce, Facebook, Twitter, and many more.

This allows users to see trends in a business through real-time graphs, numbers, and statistics while simultaneously keeping a log of changes.

Conclusion

Data engineering has taken on an increasingly important role at big tech companies of all sizes and in every industry, not just the ones that are technology companies.

It’s time to take a look at what data engineering tools are the most used by mid-sized tech companies and how different teams think about their roles in the future.

These are the most common data engineering tools in use today are based on research from different interviews with data engineers at mid-sized tech companies across all industries, as well as three full-length profiles of some of the most forward-thinking teams out there today.