Managing your data can be a complex task, and deciding on what technology to use for your data warehousing needs is a business-critical choice; the technology needs to meet your existing needs, but also be flexible, adaptable, and scalable for future developments.

Databricks is a service which takes elements of data warehouses and data lakes and combines them into a single platform. Using a cloud platform, and a common security and governance approach for all data types on an open foundation, the platform is highly rated for data science platforms and streaming analytics tools.

Read about how a Data Warehouse, Database, Data Mart and Data Lake work together.

How to manage clusters on Databricks

Databricks separates storage from compute and provides different compute options for engineering, data science and SQL workloads. For warehousing, Databricks SQL runs on SQL warehouses, including serverless, pro and classic options, while other workloads can use managed compute appropriate to the task.

Compute can be started, stopped, monitored and governed through the workspace, with policies and automation used to control access, performance and cost.

For SQL workloads, serverless compute can reduce infrastructure management, while provisioned options still allow teams to tune compute choices where they need more direct control.

The platform also automates a growing amount of performance and infrastructure management, allowing teams to spend more time on data engineering, analytics and governance rather than managing underlying compute.

The key benefits of using Databricks

There are pros and cons to using Databricks and it is often cited as being reliable, easy to set up, and suitable for users at different levels of skills in data engineering and analytical machine learning.

Some of the most commonly recommended reasons for using Databricks include:

Managed and serverless compute – Databricks now provides managed compute options designed to reduce infrastructure administration and scale resources around workload requirements.

Improved data reporting times – The Databricks platform allows for large amounts of data to be processed each hour and can see much faster data reporting times compared to other platforms.

Provides an integrated workspace – The collaborative environment of the program streamlines processes, allows for interactive creation of dynamic reports, and allows for teams to use the space and interact with the data simultaneously.

Works with Agile processes – Because Databricks has been designed for ease of access and use, and allows for multiple tasks to be created and developed through the notebook environment, the platform works well with Agile data science processes.

Challenges related to using Databricks

Although the system is robust and suitable for a wide range of users, the platform may not suit everyone. Some of the cons reported when using Databricks include:

Platform breadth – Databricks supports a wide range of workloads, which can increase the amount of architecture, governance and cost management teams need to understand as adoption grows.

Skills and operating model – notebooks remain an important part of Databricks, but production solutions also use SQL, jobs, pipelines, repositories and APIs. Teams still need appropriate engineering practices to move work reliably from development into production.

Cloud-first experience – Databricks is designed as a cloud platform, so organisations with strict offline or on-premises requirements need to consider how that fits their wider environment.

Cloud and regional availability – Databricks is available across AWS, Microsoft Azure and Google Cloud, although individual capabilities and availability can vary by cloud and region.

What type of data is Databricks ideal for?

The flexibility in Databricks allows for both structured and unstructured data, as well as semi-structured data, such as images, audio, documents, and video files. Databricks is largely used for building, testing, and deploying applications and for analytics, and does allow for unstructured data to be ingested into the lake house with a scalable auto-loader.

The tools allow for ETL (Extract, Transform, and Load) experiences and make use of Apache Spark to handle the processes.

Some of the data formats that can be handled by the platform include:

• CSV

• JSON

• XML

• Parquet

• Delta Lake

Alternatives to Databricks

Databricks for data warehousing is not the only option, other popular alternatives offering a similar service (either as a data warehouse or a data lake) include:

Amazon Web Services (AWS) – This platform is considered to require more technical knowledge than Databricks, but it also has an enormous selection of services and functionality, which also allows the platform to integrate with different cloud-based programs.

Microsoft Fabric / Azure – Microsoft Fabric now provides an integrated SaaS analytics platform with OneLake, data engineering, data warehousing and Power BI, while Azure Synapse Analytics remains available for organisations using established Azure analytics architectures.

Considerations when choosing data engineering technologies

When making your technological decisions, it is important to consider not only your immediate needs and requirements, but also those that will come – and whether the platform has the flexibility, scalability, and adaptability to cope with changing processes, coding languages, and operations.

It is important that you are working with a team that understands and is comfortable using the platform, and the coding language/s that are required for the tasks.

Databricks does offer a fast, cost-effective and scalable solution, and allows for teams to collaborate on the platform. If you need advice or assistance in determining whether this platform is suitable for your needs, we are happy to provide help.

Databricks for data warehousing with Ardent

At Ardent, we have leveraged Databricks and many other innovative technologies to deliver excellence to our clients. Whether you have a preferred technology stack or would need recommendations based on your specific data warehousing needs, we have the expertise to help. Our data engineers are proficient in world-leading data warehousing technologies to deliver our data warehousing solutions. Discover how our clients are succeeding in our collection of data success stories.