Data Engineer and Databricks

This is my first article here, I'll try to share my experience and cover topics related to data engineer, Python and Scala

As the world becomes increasingly data-driven, the role of data engineers has gained a huge importance. These professionals are responsible for designing, building, and maintaining the data pipelines that fuel modern business decision-making processes. Databricks, a leading provider of cloud-based data engineering and analytics solutions, offers a suite of tools that enable data engineers to excel in their daily tasks. In this article, I would like to write about Databricks, a powerful platform, and share my experience as a data engineer who is using Databricks daily to build a complete data solution.

Firstly, let me define what a data engineering is. Data engineering is the process of designing, building, and maintaining the infrastructure and systems that enable the ingestion, processing, and storage of large volumes of data. Data engineers work closely with data scientists, analysts, and other stakeholders to ensure that the right data is available in the right format and at the right time to support business decisions.

Databricks is an excellent fit for data engineers due to its complete suite of tools. The Unified Data Platform provides a unified (as the name suggests) environment for data engineering, data science, and business analytics. This platform allows data engineers to design, build, and manage data pipelines that can handle the most demanding workloads. Databricks also offers a variety of other tools, such as Spark, and Delta Lake, which provide powerful capabilities for data processing, storage, and management.

One of the latest innovations from Databricks is Unity Catalog (UC), a powerful tool that simplifies the management and discovery of data assets. Unity Catalog provides a centralised repository for all data-related assets, including tables, functions, and models. This tool enables data engineers to easily search, explore, and share data assets, streamlining the data engineering process and improving collaboration across teams. By providing a single, unified view of all data assets, Unity Catalog helps data engineers to work more efficiently and effectively.

For those looking to get started with Databricks and data engineering world, here are a few tips to keep in mind:

1. Familiarise yourself with Databricks' products and services: Take the time to learn about Databricks' various tools and services, including the Unified Data Analytics Platform, Spark, Delta Lake, Unity Catalog and a cloud like AWS or Azure. Understanding the capabilities of these products will help you to make decisions when designing and building your data pipelines.

2. Leverage the power of Unity Catalog: This is a game-changer for data engineers, and I must confess I took some time to enable UC in my existing workspaces. By using this tool, you can better organise your data management and discovery processes, making it easier to find and work with the data you need. It contains powerful functions, like column level permissions, data masking, etc.

3. Embrace automation: Automation is the key to efficient data engineering. Look for ways to automate repetitive tasks and processes, use Terraform and CI/CD, Databricks has a Terraform provider. Free up your time to focus on more strategic work.

In conclusion, Databricks is an excellent choice for data engineers, offering a comprehensive suite of tools and services that enable you to design, build, and manage data pipelines easily. Unity Catalog, in particular, is a powerful tool that simplifies the management and discovery of data assets.