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An increase in cloud tools and the need to process a mountain of raw data created a significant rise in demand for data engineers. Data engineers build data pipelines. They’re also the backbone of data infrastructure design and algorithm development. Data engineers are essential for making data more useful to companies.
To build such a rich data infrastructure, data engineers require a mix of different programming languages, data management tools, data warehouses, and whole sets of other tools for data processing, data analytics, and AI/ML.
This post will highlight the top 10 tools that data engineers use for building effective, efficient data infrastructure.
The Not-so-Secret Data Engineering Tools
Python is a popular general-purpose programming language. It’s easy to learn and has become the de-facto standard when it comes to data engineering.
Python can be called the Swiss army knife of programming languages due to its multiple use cases, especially in building data pipelines. Data engineers use Python to code ETL frameworks, API interactions, automation, and data munging tasks such as reshaping, aggregating, joining disparate sources, etc.
A simple syntax and an abundance of third-party libraries are other benefits of Python. Most importantly, this programming language helps decrease development time, which results in fewer expenses for companies. These days, Python is a must-know programming language in over two-thirds of data engineer job listings.
Querying is the bread and butter for all data engineers. SQL (Structured Query Language) is one of the key tools used by data engineers to create business logic models, execute complex queries, extract key performance metrics, and build reusable data structures.
SQL is one of the most important tools that help access, update, insert, manipulate, and modify data using queries, data transformation techniques, and more.
PostgreSQL is the most popular open-source relational database in the world. One of the many reasons for PostgreSQL’s popularity is its active open-source community–it’s also not a company-led open-source tool like DBMS or MySQL.
PostgreSQL is lightweight, highly flexible, highly capable, and is built using an object-relational model. It offers a wide range of built-in and user-defined functions, extensive data capacity, and trusted data integrity. Specifically designed to work with large datasets while offering high fault tolerance, PostgreSQL makes an ideal choice for data engineering workflows.
MongoDB is a popular NoSQL database. It’s easy-to-use, highly flexible, and can store and query both structured and unstructured data at a high scale. NoSQL databases (such as MongoDB) gained popularity due to their ability to handle unstructured data. Unlike relational databases (SQL) with rigid schemas, NoSQL databases are much more flexible and store data in simple forms that are easy to understand.
Features such as a distributed key-value store, document-oriented NoSQL capabilities, and MapReduce calculation capabilities make MongoDB an excellent choice for processing huge data volumes. Data engineers work with a lot of raw, unprocessed data, making MongoDB a classic choice that preserves data functionality while allowing horizontal scale.
5. Apache Spark
Businesses today understand the importance of capturing data and making it available within the organization quickly. Stream Processing allows you to query continuous data streams in real-time–including data such as sensor data, user activity on a website, data from IoT devices, financial trade data, and more. Apache Spark represents one such popular implementation of Stream Processing.
An open-source analytics engine known for its large-scale data processing capabilities, Apache Spark supports multiple programming languages, including Java, Scala, R, and Python. Spark can process terabytes of streams in micro-batches and uses in-memory caching and optimized query execution.
6. Apache Kafka
Similar to Apache Spark, Apache Kafka is an open-source event streaming platform with multiple applications such as data synchronization, messaging, real-time data streaming, and more. Apache Kafka is popular for building ELT pipelines and is widely used as a data collection and ingestion tool.
A simple, reliable, scalable, and high-performance tool, Apache Kafka can stream large amounts of data into a target quickly.
7. Amazon Redshift
In a modern data infrastructure, data warehouses have evolved beyond data storage roles. Amazon Redshift is an excellent example–it is a fully-managed cloud-based data warehouse designed for large-scale data storage and analysis.
Redshift makes it easy to query and combine huge amounts of structured and semi-structured data across data warehouses, operational databases, and data lakes using standard SQL. It also allows data engineers to easily integrate new data sources within hours, which reduces time to insight.
Snowflake is a popular cloud-based data warehousing platform that offers businesses separate storage and compute options, support for third-party tools, data cloning, and much more. Snowflake helps streamline data engineering activities by easily ingesting, transforming, and delivering data for deeper insights.
With Snowflake, data engineers do not have to worry about managing infrastructure, concurrency handling, etc., and can focus on other valuable activities for delivering your data.
9. Amazon Athena
Amazon Athena is an interactive query tool that helps you analyze unstructured, semi-structured, and structured data stored in Amazon S3 (Amazon Simple Storage Service). You can use Athena for ad-hoc querying on structured and unstructured data using standard SQL.
Athena is completely serverless, which means there’s no need to manage or set up any infrastructure. With Athena, you do not need complex ETL jobs to prepare your data for analysis. This makes it easy for data engineers or anyone with SQL skills to analyze large datasets in no time.
10. Apache Airflow
With the emergence of multiple cloud tools in a modern data workflow, managing data between different teams and achieving data’s full potential becomes more challenging. Job orchestration and scheduling tools strive to eliminate data silos, streamline workflows, and automate repetitive tasks so that IT departments can move quickly and efficiently. Apache Airflow has been a favorite tool for data engineers for orchestrating and scheduling their data pipelines.
Apache Airflow helps you build modern data pipelines through efficient scheduling of tasks. It offers a rich user interface to easily visualize pipelines running in production, monitor progress, and troubleshoot issues when needed.
Building the Best Data Toolkit
These were just 10 of the top tools. There are tons of other data tools out there, which can make data engineers spoilt for choice. While these tools help data engineers build an efficient data information infrastructure, they also have their own pros and cons. Data engineers must find the best data tools for their companies while managing tools’ drawbacks. Ultimately, the goal is to build a robust stack that systematically handles data and can work for months or years with minimal tweaking.
Amazon Cloud Services and MongoDB are sponsors of InApps.
Featured image via Pixabay.