Data Engineering with Databricks

Digital data[1] is a valuable asset for many businesses in the 21st century. It boasts real-world applications in all types of sectors and industries. And with organizations shifting from conventional architectures to contemporary data structures, data engineering with Databricks becomes a critical service. It helps create data pipelines with new appropriate technologies that may scale and operate in the cloud.

The wide selection of tools available in the data engineering space can be overwhelming for data professionals to comprehend. Each offers an exciting promise–faster pipelines, simplifying your workflow, revealing machine learning capabilities, or deeper analytics and insights. With the release of every new product, there’s a new ‘magic bullet’ offered to transform your company’s data engineering department.

So how do you pick the best data engineering tool for your needs? In this guide, we look at an established open-source platform called Databricks, which is among the first of its kind in the market. We also provide tips on its application in machine learning and data engineering, and look at what sets it apart from other tools offering a similar promise.

Introduction to Databricks for Data Engineering

What is Data Engineering?

To understand what data engineering entails, you need to focus on the ‘engineering’ part of it. Typically, engineers design and create things. Therefore, data engineers design and create the IT infrastructure that enables converting big data into a highly usable form. This makes it easy for other end users like data scientists to understand and interpret it.

There are myriads of tools that analyze data on a large scale. This has prompted organizations to launch significant machine learning projects. But a lot of these projects are not successful because the data is unclean or unusable. Thus, a lot of emphasis is placed on data engineering to make the data more precise and usable – that’s what data engineer do.

Read more about Data Engineering in Startups: How to Manage Data Effectively

What is Databricks?

This is a cloud-based machine learning and data engineering platform. It was named as the leading data science and machine learning platform by Gartner’s 2021 Magic Quadrant for two consecutive years[2].

The team that built Apache Spark is the one behind the Databricks platform. Users can employ this platform to run tasks on Apache Spark as it makes its architecture easy to use. Below are some of its core benefits:

Cloud-agnostic

If your business is cloud-based, chances are it’s based on one of these three platforms:

• Google Cloud
• AWS
• Microsoft Azure

Whether you’re operating on Google Cloud, AWS, or Microsoft Azure platform, you can easily integrate Databricks into your existing subscription. And if you want to migrate to another platform, say from Google Cloud to Microsoft Azure, you can relocate with Databricks alongside your entire cloud architecture without any operational challenges.

Read  more: Introduction to Big Data Platforms

Large-scale processor

The core architecture of Databricks functions on Apache Spark. This is an open-source analytics platform with an emphasis on performing numerous tasks simultaneously. A key highlight of the Spark architecture is the driver/worker node system. With this system, you can combine several servers into one.

Basically, all worker/executor nodes perform a similar task, piece-by-piece. Upon finishing its task, each server relays back the feedback to the main server known as the driver/master node. Here, everything is combined together for the ultimate output.

Support for several programming languages

Given the proliferation of several types of data in different formats, data engineers need a toolset that takes into account all those varying needs. This entails using a suitable coding language and its features for the right task.

The support for several coding languages and data pipelines is arguably the killer feature for data engineering with Databricks. Data engineers can create code in their preferred coding language.

Options include:

• Python
• Scala
• SQL
• R

If you want to do a bit of string manipulation, you may use Python or a language like Scale, which requires object-oriented support. And if you need to manipulate relational data, you can use SQL. The Databricks platform allows data engineers to write code using the aforementioned languages in a similar process. This was initially not possible, before until now.

How Databricks integrates into contemporary data architecture

A contemporary data framework typically features these 3 essentials:

• Extract/load/transform (ELT): It’s the process of extracting and transmitting data from one or multiple sources and loading it into the main data repository.
• Extract/transform/load (ETL): Unlike ELT, ETL converts each source of raw data into a dimensionally modeled structure.
• Analytics/reporting: This is a platform for accessing curated data.

Data engineering with Databricks has been applied mainly on ELT and ETL tasks. For organizations with an existing cloud-based data warehouse, Databricks would best fit in there as part of their data framework. Databricks facilitate the movement and conversion of data from a raw source to a warehouse. In some cases, it may extend to the analytics/reporting layer.

Data Lakehouse Architecture with Databricks

When it comes to the Lakehouse framework, Databricks is deployed in ELT and ETL tasks as well as storage (in the form of the data lake or data warehouse). This is why the framework is named ‘Lakehouse’.

With the Lakehouse architecture, users can perform everything on the same platform, including:

• SQL
• Business intelligence
• Data science
• Machine learning

You don’t necessarily have to adopt the full Databricks Lakehouse framework to enjoy the platform’s benefits. You can choose which features to adopt based on your needs and internal capabilities.

It might be interesting for you: What is the future of data engineering?

Summary

Data engineering services have become an important step for business data warehouses and machine learning activities. While it isn’t new, it has become increasingly complex with the inclusion of non-relation data. Thus, data engineers are forced to integrate non-relation tools into their toolset.

This is why data engineering with Databricks is the new favorite norm. Databricks is heads and shoulders above its competitors as a data engineering tool due to its open-source technology, ability to create code in multiple languages, and an effective user interface allowing collaborations between different developers on similar code.

Data Engineering with Databricks: FAQ

What is the main responsibility of data engineer?

Data engineering involves designing and creating IT infrastructure to convert big data into a highly usable form, making it accessible for analysis by data scientists and other end-users. It’s crucial for data engineer and data engineer associate as it ensures that data is clean, precise, and usable for various purposes, including machine learning and analytics.

How Databricks faciliates the data engineers’s workflow?

Databricks is a cloud-based machine learning and data engineering platform, known for its ease of use and integration with Apache Spark. It stands out for data engineers and data engineer associates due to its cloud-agnostic nature, large-scale processing capabilities, support for multiple programming languages (Python, Scala, SQL, and R), and integration into contemporary data architectures.

How does Databricks smoothen he collaboration between data engineer and data engineer associate?

Databricks is primarily applied in Extract/Load/Transform (ELT) and Extract/Transform/Load (ETL) tasks within contemporary data architectures, making it essential for data engineers and data engineer associates. It facilitates the movement and conversion of data from raw sources to data warehouses, and in some cases, extends to the analytics/reporting layer.

What is the Lakehouse framework, and how does Databricks fit into it for data engineers and data engineer associates?

The Lakehouse framework involves deploying Databricks in ELT and ETL tasks, storage (data lake or data warehouse), SQL, business intelligence, data science, and machine learning. It allows data engineers and data engineer associates to perform various data-related tasks on a single platform, providing flexibility and efficiency.

Why is data engineering with Databricks considered the new norm for data engineers and data engineer associates?

Data engineering with Databricks is increasingly favored by data engineers and data engineer associates due to its open-source technology, support for multiple coding languages, and user-friendly interface facilitating collaboration among developers. It addresses the growing complexity of data engineering tasks, especially with the inclusion of non-relational data, making it a preferred choice for many organizations.

This article is an updated version of the publication from  Dec 14, 2021.

References

[1] Economist.com. The Most Valuable Resource is no longer Oil but Data. URL: https://www.economist.com/leaders/2017/05/06/the-worlds-most-valuable-resource-is-no-longer-oil-but-data. Accessed December 8, 2021.
[2] Databricks.com. Databricks Named a Leader in 2021 Gartner Quadrant for Data Science and Machine Learning Platforms. URL:  https://databricks.com/blog/2021/03/04/databricks-named-a-leader-in-2021-gartner-magic-quadrant-for-data-science-and-machine-learning-platforms.html. Accessed December 8, 2021.