Databricks vs. Snowflake: Which is Better for Data Engineering?

As you know rapid evolution is taking place in data engineering. Therefore, two platforms dominate the world of data engineering is that of Databricks vs Snowflake. You can easily manage and process large-scale data through both of these technologies. In other words, both of these technology provide robust solutions, but they do cater to unique needs and workflows. Through this blog, you can decide which platform best suits your data engineering needs. This comparison further aids you in making an informed choice at once.

Understanding Databricks and Snowflake: What is it?

Databricks

In recent years or so, Databricks as a data engineering platform gained huge prominence. It is an open-source, cloud-based, and Apache Spark-based platform, that is solely engineered to process big data, machine learning, and analytics. Further, it provides an optimized environment for all sorts of AI-driven applications, machine learning, and ETL (Extract, Transform, and Load). Businesses looking to optimize their data infrastructure can hire Databricks developers to fully leverage its capabilities.

Key Features

Some of the key features of Databricks are as follows

1. Databricks is fully built on Apache Spark. Further, it is optimized to process large-scale data at once.
2. It is also designed on Lakehouse architecture. Therefore, it combines the benefits of both data lakes and data warehouses at the same time.
3. This software has both ML and AI capabilities. Further, it is integrated with MLflow for machine learning lifecycle management.
4. The software supports multi-clouds. Further, it works across both AWS, Azure, and Google Cloud.
5. It works on Delta Lake, therefore, it ensures reliability, performance, and version control of data lakes.

Snowflake

Unlike Databricks, Snowflake also earned huge prominence in recent years or so. This software is designed fully for SQL-based analytics and Business Intelligence applications. This software, in other words, is a cloud-native data warehouse, that excels in scalability, ease of use, and performance. Companies aiming to streamline their data warehousing solutions can hire Snowflake developers to maximize Snowflake’s efficiency.

Key Features

1. Through Snowflake you can separate computing and storage. Further, it allows independent scaling for better cost optimization.
2. It also deals with automatic performance optimization and doesn't require any sort of manual testing.
3. Further, it is ideal for BI users and analysts. It also supports SQL-based processing.
4. Also, it supports secure data sharing. Through this platform, you can easily share data across accounts and organizations.
5. It is also available on AWS, Azure, and Google Cloud. Further, deals with Multi-cloud deployment.

Related Read - AWS API Connect to Databricks Warehouse

Head-to-Head Comparison: Databricks vs. Snowflake

When it comes to head-to-head comparison of both Databricks and Snowflake. You should understand the subtle difference in characteristics that exists between both these softwares. In other words, the fine choice fully depends on your specific business needs, use cases, and expertise. Further, in numerous modern data architectures, the organization uses Databricks for data engineering and ML. Whereas, Snowflake is used for analytics and reporting

Primary Use Case

In primarily used cases Databricks is designed for big data processing, ML and AI.

Whereas, Snowflake is designed for SQL Analytics, and Data Warehousing.

Compute Model

When it comes to computing models, Databricks works on cluster-based Apache Spark. Whereas, Snowflake works on auto-scaling, and multi-cluster.

Storage

Databricks storage takes place in the data lake. When it comes to Snowflake, the storage takes place in Columnar storage.

Performance Optimization

Databricks severely require some sort of tuning. Whereas Snowflake is auto-optimized by nature.

Ease of Use

Databricks requires expertise both in Spark and PySpark. When it comes to Snowflake it is SQL-friendly by nature, and easy to use for analysts.

Multi-cloud Support

Databricks and Snowflake provide multi-cloud support. Therefore, both of the software works on cloud platforms like AWS, Azure, and GCP.

Security & Compliance

Databricks provides both role-based access and data governance tools. Snowflake, on the other hand, provide strong security and comes with automatic encryption.

Pricing Model

The pricing model of Databricks is pay-as-you-go computing and storage. Whereas, the pricing model for Snowflake is pay-per-use with independent scaling.

Which One is Better For Data Engineering?

As you know to choose the appropriate tool for data engineering. Both of these tools are powerful platforms but serve different purposes. If you are building AI-driven solutions or handle huge ETL pipelines, Databricks happens to be the best choice for you. While, if your focus is on SQL-based analytics and a hassle-free data warehouse, Snowflake is the only option you got. Through this discussion, you can find out which one suits you and serves your purpose best in the field of data engineering.

Databricks- Your Only Choice

You can choose Databricks as the best data engineering platform if it serves your purpose. Try to choose it, if you fully work with big data, machine learning and Artificial intelligence. Even you need high-performance ETL processing and real-time data transformations to serve your purpose. Databricks can also be your first and last choice if you prefer open-source and Apache Spark-based solutions. Further, data bricks are required by you for advanced analytics, complex transformations and streaming data.

Snowflake–Your Last Resort

If Databricks doesn't serve your purpose. Then you can opt for Snowflake as the best data engineering platform. If only your sole focus is on SQL-based analytics and business intelligence. You can opt for Snowflake if you want a fully managed solution with minimal maintenance work. Choose Snowflake, if you need simple, yet cost-effective scaling for data warehousing. Snowflake aids you in prioritizing data sharing, security and compliance.

Final Thoughts

From the above discussion, it is clear that you can opt between Databricks vs Snowflake solely depending on your organization's special requirements. If you plan to deal with big data processing, machine learning and complex ETL workflows, then Databricks is your only option. On the other hand, if you plan to deal with SQL-based analytics with minimal operational overhead. Then Snowflake is your ideal choice. Ultimately, there are numerous businesses, that integrate both platforms just to leverage their strengths, ensuring nothing but a flexible & robust data infrastructure.