Apache-Spark-vs-Databricks-Unified-Analytics-Platform

Apache Spark and Databricks Unified Analytics Platform are ‘big data’ processing and analytics tools. Apache Spark is an open-source general data processing engine. On the other hand, Databricks Unified Analytics Platform is a paid analytics and data processing platform designed to make use of Apache Spark, though it adds additional support, services, and features. 

Both Apache Spark and Databricks Unified Analytics Platform are primarily used by large enterprises, with a significant user base among mid-sized companies as well. Both tools focus on big data processing, often making them overkill for the needs of smaller businesses.

Features

Apache Spark is a core component of Databricks Unified Analytics Platform, which means that it’s difficult to compare them directly. Essentially, an organization would not be able to use Databricks Unified Analytics Platform without also using Apache Spark. In this section, we’ll examine the advantages of Apache Spark as a general data processing engine, then discuss the benefits of Databricks Unified Analytics Platform as a platform.

Apache Spark is designed to be a lightning-fast data processing engine with multiple use cases. Its in-memory processing design means it can run with very few disk read/write operations, which helps it run quickly even on enormous datasets. Developers report that its SQL interface and object-oriented design make it intuitive to understand and write code for. Users also appreciate its rich set of APIs for cluster management and ETL procedures. As an open-source tool with wide industry adoption, Apache Spark has a large support community and plenty of recommended solutions to common problems—and, of course, it’s free.

If Apache Spark is the engine, Databricks Unified Analytics Platform is the whole car: a full-service data analytics solution with collaboration features, machine learning tools, data lake, and data pipeline capability. The service simplifies and streamlines the setup and maintenance of Apache Spark clusters, adding data security and automatic cluster management features. It supports multiple different languages, such as Scala and Python, making it easy for developers to create data pipelines in languages they’re comfortable with. It also adds integrations for applications and services such as Microsoft Azure or AWS. Dedicated customer support teams assist clients with custom features or exceptions, tailoring the platform to their needs.

Limitations

Consider the limitations of Apache Spark and Databricks Unified Analytics Platform before adopting one or both of them. 

As a standalone tool, Apache Spark requires supporting tools to fill in capability gaps. For example, users will need to provide a database infrastructure to store the information Apache Spark works with, which requires separate expertise and development. Apache Spark’s in-memory processing may be fast, but it also means high memory requirements, which can get expensive very quickly. Some users found that the tool isn’t well-suited for real-time analytics, while others wished for more integrated data security features. Finally, Apache Spark may be designed intuitively, but it’s still a complicated piece of software with a significant learning curve. And since it’s open-source, there’s no dedicated training or customer support.

Databricks Unified Analytics Platform offers additional features and services. However, for businesses with smaller datasets or more focused processing needs, the full-service platform may be more than they need. Although its UI is intuitive, some Databricks Unified Analytics Platform users suffered from long loading times or problems with language interpreter settings. Other users found the platform’s in-house documentation insufficient and ended up resorting to outside sources for support. Databricks Unified Analytics Platform also isn’t free; businesses will have to pay for the amount of processing they need.

Pricing

Apache Spark is open-source and free to download.

Databricks Unified Analytics Platform offers tiered pricing based on per-second usage. Pricing varies depending on the service tier and cloud platform (Azure or AWS) used. More pricing information is available on the vendor’s website.