Executive Summary
Data engineers build pipelines that help companies collect, merge, and transform data to facilitate seamless analytics. They oversee the creation of an infrastructure design that enables modern data analytics.
A Data engineer uses different tools and technologies to extract, transform and load (ETL) data.
What are Data Engineering Tools?
According to recent reports, Data engineering is one of the highest in-demand jobs. Data engineers manage a very large amount of data using various data engineering tools and frameworks.
Data engineering tools help in building pipelines and designing algorithms that are easier and faster.
Database frameworks like SQL, and NOSQL helps to acquire, analyze and manage huge amount of data.
Big data can be structured, unstructured or can be in any format. Therefore, we need data engineering tools to transform into the desired format.
Data ingestion tools such as Kafka offer quick data ingestion and locate appropriate data sources and analyze them.
5 Data engineering tools worth Exploring in 2023
1. Apache Spark
Apache Spark is an open-source data analytics engine. It is one of the fastest platforms for stream processing and data management. It can handle big data sets and can share processing tasks amongst multiple devices.
Spark can analyze and handle data stored in Hadoop clusters and change data in real-time using spark streaming.
2. Apache Kafka
Kafka is one of the leading engineering tools among data engineers. It helps in creating pipelines using real-time streaming data. Besides the above, it allows data synchronization and messaging. It acts as an intermediate by receiving data from source systems and making it available in real time to the destination.
It has a feature to safeguard clusters from node failure.
3. Azure Data Factory (ADF)
ADF is a serverless data integration solution to gather, process and modify all data at scale. While shifting data to Azure, ADF acts as an ideal option for migrating existing ETL. ADF is a scalable platform while parallelism and time-slicing options, help users to transfer large chunks of data to the cloud. We can deploy, execute and manage ETL packages using ADF.
4. Azure Databricks
Databricks is a spark-based unified analytics engine. It provides infrastructure and modern data analytics support to data professionals like data analysts, data engineers and data scientists. It is used for its interactive and shared workspace. It helps to collaborate on real-time coding in notebooks written with Python, Spark, R and Scala.
It allows paying only for an active cluster with auto-scaling, and auto-terminating options. It allows us to connect with various data sources and perform ML algorithms.
5. Apache Airflow
One of the trending data engineering tools helps in managing, scheduling and building pipelines. It allows the smooth orchestration of data pipelines. It lets users establish granular workflows and monitor progress as they happen. It manages multiple jobs at once. We can upgrade it using plugins present on the UI. We can connect to external systems like databases, and cloud providers using code templates.
Conclusion
Here we discussed 5 tools. There are tons of other data engineering tools which data engineers spoilt according to their requirements. While these tools help engineers to build proper data information infrastructure, they also have their pros and cons. We need to find the best data tools for our best use while managing the drawbacks of tools. The goal is to build robust stack which handles data systematically and can work with minimal tweaking.