Databricks vs Snowflake – An Interesting Evaluation

When we talk about the world influenced heavily by data infrastructure, two cutting-edge data tools are often referred to – Snowflake and Databricks. They both represent two data-dependent areas, with a modern-day touch and offer support for cloud infrastructure through Azure, Google Cloud, AWS.

It all started with the implementation of Enterprise Data Warehouse (EDW) and Data Lake. With time, Snowflake came up with a modernized version of EDW, and Databricks came up with an enhanced version of Data Lake.

EDW came in the 1980s and organizations depended on data for critical business decisions. It stored data that was structured, had centralized processing and storage. Snowflake took on the EDW concept and converted it into a modern-day, fully managed cloud-based tool.

Data Lake solutions came in the 2000s when there was too much data to handle. Data Lakes started storing all of it in its native format, for organized usage later. It had all unstructured data with decentralized storage and processing. Databricks took on the Data Lake concept and converted it into a cloud-driven, modernized version.

Today, Databricks vs Snowflake is an interesting comparison being made that showcases certain similarities but with distinct characteristics of their own. Before we compare them both, let us have an individual look at them.

What Is Databricks?

Databricks combines the best of data warehouses and data lakes into a data lake house architecture. It collaborates on all your data, analytics, and AI workloads.

Databricks comes from the founders of Apache Spark, and it performs data science and engineering all around the Machine Learning systems, starting from preparing data to experimentation and managing configurations.

What Does Databricks Do?

Databricks is basically a superior and adaptable tool that is being leveraged by leading organizations. It is ideal for machine learning and data science workflows. There is minimum vendor lock-in, and data can be kept wherever the user wants to. It just needs to be connected to Databricks and the same procedure is followed.

Azure Databricks is a ‘first party’ Microsoft service, a data analytics platform specially optimized for Microsoft Azure cloud services platform. When Databricks is moved to a Microsoft cloud instance, it is Azure Databricks – a jointly created cloud data service from Microsoft and Databricks, meant for data science, data analytics, data engineering, and Machine Learning.

Databricks Features:

• Visualization, integration, and compliance
• Notification and logs
• Jobs scheduler
• Integrated identity management
• Auditing, secure deployment
• Databricks runtime, interactive exploration
• High productivity and collaboration
• Wide list of data sources

Organizations Using Databricks:

Comcast, HSBC, T-Mobile, CVS Health, Shell, Regeneron, QuintoAndar, GIPHY, Compile Inc, Iziwork, Auto Trader, ClearBank, and many more.

Snowflake Data Warehouse, What It Is?

Snowflake enables you to build data-intensive applications without an operational burden. Trusted by fast-growing software companies, Snowflake is a unified platform that showcases a great cloud experience, with multiple workloads and no data silos.

It removes the management and administrative hassles that are related to managing traditional warehouses and varied big data platforms. It is an effective data warehouse-as-a-service that executes on AWS and there is hardly any infrastructure management.

It has enhanced itself to become a much better version of EDW. It has been a great alternative to business intelligence analytics and workloads. It is simple to use and since it is cloud-driven, there is not much infrastructure management or hardware management. Through the data cloud, users can access a world of data and services. There is modern-day data governance and security available to users.

Users have the facility for building and driving business ahead based on valuable data. It caters to data engineering, data science, data applications, data sharing – almost everything related to data.

Snowflake Features:

• Scalable and needs least administration
• Cloud provider agnostic
• Workload separation and concurrency
• Semi-structured data
• Data storage and analytics
• Reliable, cost-effective, and durable
• Optimized table structures
• Sharing data within accounts

Organizations Using Snowflake:

Immuta, Hawaiian Airlines, Shipt, Albertsons, Kemper, Instacart, Lime, Square, Postclick, Rent the Runway, Deliveroo, and many more.

Snowflake vs Databricks: An Evaluation

As we watch this interesting comparison, here are a few elements that are common to both, followed by parameter-driven differences between both.

Databricks And Snowflake Similarities:

• No fixed limit for data scalability
• Separate storage and compute facility
• Cost-based optimization of queries
• Fixed view dashboards and reporting
• Freedom to select number of nodes
• Good performance on large data sets
• Usage-based billing system
• No entry-level setup charges
• Available devices – Windows, Mac, Web-based
• Ideal for small, medium business, and large enterprises
• Modern-day approach to cloud data warehousing

Databricks vs Snowflake: The Differences