Top 14 tools for Data Engineering

In this article, we will see the tools for data engineering work when implementing data pipelines in your company or business.

These top 14 tools help companies of all sizes integrate, cleanse, and analyze structured and unstructured data from any source so you can focus on actually working with the results and not on making sure it’s actually possible to use your data.

What are the Data Engineering tools?

Data engineers have to use a variety of programs and applications in order to get the data ready for data scientists.

Some of these tools are well-known, while others might be new or even underutilized by other teams.

You need to learn these tools and get the use of each tool so you can learn them all quickly and start making awesome things with your data.

While SQL is a very popular tool that anyone working with data has heard of, it’s by no means the only tool in a data engineer’s toolbox.

The ability to use SQL and Python is a great start, but other tools such as Hadoop and Spark can handle bigger datasets than SQL.

For example, Apache Pig is similar to MapReduce, which Google originally developed to process large amounts of data. Other common tools include Hive and Impala.

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

Top 14 Tools for Data Engineering

Here is the list of the top 14 tools that data engineers need to know to get a master’s in the world of Data Engineering.

Related Article: Top 17 Best Cloud Migration Tools

1) PostgreSQL

PostgreSQL is an open-source relational database system that supports a broad variety of use cases, making it great for data engineering projects.

It also provides powerful features such as geospatial support and JSON/JSONB, which can simplify your project’s requirements.

If you need a more SQL-like experience than MongoDB offers, PostgreSQL might be a good fit.

It’s worth noting that if you are working with sensitive information, you should encrypt your data in PostgreSQL to prevent unauthorized access.

To learn more about encryption with PostgreSQL, check out our tutorial on using SSL certificates to secure databases.

2) Apache Spark

Spark is a cluster computing tool that allows you to run fast, distributed data processing jobs.

It’s similar to Hadoop MapReduce in that both tools are used to manage big data sets, but Spark doesn’t require as much disk space or memory, making it an ideal option when disk storage is limited or you need greater processing speed.

Many companies use Apache Spark alongside Hadoop because of its ability to handle large-scale data processing.

This makes it a good choice for companies that want to process their large datasets quickly and on-demand.

3) PySpark

Spark provides Python bindings to Apache’s Spark, a general-purpose data processing engine with fast iteration over large datasets.

With PySpark, you can write code that runs both on Hadoop clusters and on local machines (and also supports R).

PySpark also supports a number of other languages as well, This means that if you need to integrate tools or existing code, it’s easy to do so with PySpark.

4) Jupyter Notebook

Jupyter Notebook is a browser-based environment that enables interactive computing across many programming languages.

Because it’s hosted online, Jupyter Notebook can be used anywhere, with any device, and without installation or configuration.

Jupyter Notebook runs Python code cells in its environment and produces interactive documents called notebooks.

You can edit your notebooks from within your browser or from your local computer with a program like Microsoft Word or Google Docs. One of many tools for data engineering

6) Hive & Impala

Two of Hadoop’s most well-known products are Hive and Impala. While they serve similar purposes, they have different approaches to data analysis.

Hive is a query engine that allows you to write SQL queries that can process large amounts of data in HDFS.

Impala is designed specifically for batch processing on a single node, which makes it more efficient than Hive.

If your business works with large amounts of data or needs to run many queries at once, Impala may be a better option than Hive.

7) Amazon Redshift

Amazon Redshift is a fast, scalable, fully managed, petabyte-scale data warehouse service that simplifies your life and gives you power over all your data with your existing business intelligence tools.

With just a few clicks in Amazon Redshift Management Console, you can launch a brand new instance of Amazon Redshift and immediately start loading your data.

Once loaded, you can use familiar tools like SQL or BI tools like Tableau to query your data. You pay only for what you use with no upfront commitments or long-term contracts.

5) AWS Elastic MapReduce (EMR)

Amazon Web Services (AWS) Elastic MapReduce (EMR) is a web service that runs Hadoop in Amazon’s cloud.

There are two pricing options for EMR: you can run on virtual servers or have dedicated hardware. With EMR, you pay only for what you use and are billed monthly.

Other top-10 tools include Cloudera, Hortonworks, IBM BigInsights, Microsoft Azure HDInsight, and others.

Here are some resources to help you choose which tool is best for your company: Choosing an Open Source Tool to Do Data Engineering, How to Choose Between Apache Hadoop Versions 1.x and 2.x (and 3).

AWS Introduces Dedicated EC2 Instances for its Cloud-Based Big Data Processing Service, What is Spark? An Introduction from Netflix’s Igor Glushkov

An example of code generated by data engineering tools would be query code written in SQL or Python.

9) Confluent Platform

With Apache Kafka at its core, Confluent Platform is an end-to-end toolset that includes Kafka Connect, which allows users to connect to a wide range of data sources, and Kafka Streams.

Both tools provide near real-time processing capabilities as well as easy integration with other platforms such as Spark or Flink.

The platform comes in three different editions: Confluent Open Source, Confluent Enterprise, and Confluent Cloud.

8) Snowflake Computing

Snowflake is an affordable, all-in-one data warehouse designed to support petabyte-scale workloads. Snowflake’s elastic architecture automatically scales with your data volume and complexity, making it possible to make predictive business decisions in real-time.

Snowflake is also a fully managed service that frees you from having to manage or worry about underlying infrastructure so you can focus on what matters most: your data.

10) Google BigQuery

Google BigQuery is Google’s fully managed, petabyte-scale data warehouse. From short-term batch workloads to long-running streaming jobs, you can ingest, transform and combine all your data in one place at up to 100 terabytes per day and run any SQL query.

There are no servers or infrastructure to manage, and it scales automatically as your data grows. You pay only for what you use with a simple pricing model that includes storage, queries, and throughput.

11) ADF (Azure Data Factory)

This tool helps you to create complex data pipelines that take advantage of common tools like SSIS and Azure Machine Learning.

It supports activities such as connecting to multiple services, SQL queries, the transformation of data from one format to another, and more.

It has a user-friendly graphical interface that makes it easy to set up complex data flows and improve collaboration between business intelligence (BI) teams, database administrators (DBAs), and line-of-business owners.

12) Informatica PowerCenter: 

Informatica PowerCenter is an ETL (extract, transform, load) tool that takes in data from various sources like databases, flat files, etc., and helps to load it into other various targets like data warehouses and analytical tools.

Use Informatica PowerCenter if you are looking for a good ETL tool. ## Hadoop: Hadoop is one of the most popular tools used by data engineers.

It’s basically a framework that enables distributed processing of large datasets across clusters of computers using simple programming models.

It also provides high-availability storage with automatic failover and redundancy across multiple nodes, and its ability to run on inexpensive hardware means it can be used at scale. Hadoop was developed by Yahoo!

13) Microsoft Azure HDInsight

Microsoft Azure HDInsight is Microsoft’s Apache Hadoop-based platform as a service (PaaS) offering, enabling customers to easily and quickly process massive amounts of data using Hadoop via a pay-as-you-go subscription model.

Azure HDInsight includes Hortonworks’s Enterprise Data Hub, along with SQL Server and MongoDB integration services.

In addition, it features an interactive querying tool called Hive Query Language (HQL), which enables business users to run queries against large datasets in an intuitive manner.

Users can also leverage Spark through R or Python scripts within Azure HDInsight through Spark on YARN capability.

14) MongoDB Atlas

MongoDB Atlas is a cloud-based database-as-service from MongoDB. It combines MongoDB’s high availability, security, and scalability with automated provisioning and management to make it easier to deploy and run in production environments.

In addition, MongoDB Atlas can be used as a standalone service or as part of an on-premises installation.

This flexibility makes it an ideal option for organizations that want to keep their data in their own data center while still taking advantage of MongoDB’s benefits.

Conclusion

Data Engineering Tools are used to make data ready for data scientists. These tools help you build data pipelines that source and transform your data into structures needed for analysis.

In short, these tools bring order to chaos, making it easier for data scientists to analyze large datasets. these tools are open-source tools that can be downloaded from Github.

They can be installed on Linux or Windows operating systems and work with big data frameworks like Hadoop.