November 23, 2021
December 7th, 2022
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.
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.
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.
Comcast, HSBC, T-Mobile, CVS Health, Shell, Regeneron, QuintoAndar, GIPHY, Compile Inc, Iziwork, Auto Trader, ClearBank, and many more.
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.
Immuta, Hawaiian Airlines, Shipt, Albertsons, Kemper, Instacart, Lime, Square, Postclick, Rent the Runway, Deliveroo, and many more.
As we watch this interesting comparison, here are a few elements that are common to both, followed by parameter-driven differences between both.
|Ownership of Data||Data processing and storage layers are completely decoupled||Owns the data processing and storage layers and does not decouple them|
|Best Fit For||Best fit for high-performance SQL queries. Allows working in different languages like R and Python.||Best fit for SQL-driven BI segments. Offers ODBC and JDBC drives for third-party integration.|
|Scalability||Load based auto-scaling||Auto-scaling up to fixed warehouses|
|Structure of Data||It can work with all data types in their initial format. Can be used as an ETL tool for adding structure to the unstructured data.||Can upload and save semi-structured/structured data, without any ETL tool. Once uploaded, it will transform data into the internal format.|
|Service||PaaS based solution||SaaS-based solution|
|Feature List||Dynamic exploration, interaction, task management, audits, analytics dashboard, notebook procedures||Repository and security competencies, safety validations, and interconnections|
|Network Security||Firewall whitelist/ blacklist control, TLS, isolation||Private Link whitelist/blacklist control, Firewall, SSL|
|Data Partitioning||Data lake with all data for ingestion and processing||Cluster keys, micro partitions, pruning|
|Usage Categories||Big Data, Data Science, Data Analysis, Machine Learning||Database management, Data Warehouse, ETL|
|Data Processing Engine||Data lake for extracting SQL and batches||Table or query results to be exported|
|Isolated Tenancy||Isolated storage and compute facility||Multi-tenant resources|
|Integration Tools||Kafka, Hadoop, Keras, TensorFlow, PyTorch, Apache Spark, Pentaho, Talend, Looker, Redis, MongoDB, Cassandra, Amazon Redshift, Tableau, etc.||Node JS, Python, Looker, Mixpanel, Liquibase, Apache Spark, Talend, Fivetran, AWS, etc.|
|Pricing||3 enterprise pricing options – Databricks for data engineering workloads/data analytics workloads/enterprise plans||4 enterprise-based plans – standard/premier/enterprise edition and enterprise edition for sensitive data|
|Architecture||Lakehouse architecture, unified data, analytics, and AI||Replacing on-premises EDW|
|Data Science and ML||Inbuilt and unified tool for any type of need||Not available|
|Data Virtualization||Flexible with any data source||Not available|
|Internal Data Storage||Standardized Parquet files||Internal data format for storage|
|User Interface||Code-driven, CLI-based Notebook interface||Simple, responsive, neat, and easy interface|
|Streaming Workload||Customizable though not integrated||Integrated streaming through Spark|
|Data Ingestion||With existing connectors and drivers||With most data sources and with Synapse connector too|
Snowflake vs Databricks comes up as an interesting evaluation and there have been vendors who are offering both of them as a combined solution. Their competencies may be clubbed into a unified offering, making it simple and effective for organizations to create and maintain data pipelines.
Organizations and business owners need to analyze their requirements, budgets, timelines, resources and choose the one that suits perfectly in creating a Data Lake or Data Warehouse, as needed. Choosing either one or a combination of both, with the help of an experienced vendor would surely benefit, bigtime in garnering the best of analytics from the world of data!
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