With more and more solutions entering the enterprise software market, organizations have used many data sources for their operational processes. To properly transfer and share your organizational data and information between software systems, using an effective ETL tool is a necessity. This resource will analyze two of the top ETL products, Databricks and Snowflake, so you can see which would better satisfy your data extraction, transformation and loading needs. (Also read TechRepublic’s ETL tools comparison article about Dremio vs. Snowflake.)
What is Databricks?
Databricks ETL is a data and AI solution that organizations can use to accelerate the performance and functionality of ETL pipelines. The tool can be used in various industries and provides data management, security and governance capabilities.
What is Snowflake?
Snowflake is software that provides users with a data lake and warehousing environment for their data processing, unification and transformation. It is designed to simplify complex data pipelines and can be used with other data integration tools for greater functionality.
Databricks vs. Snowflake software comparison
Which has better integration and synchronization?
The Databricks solution allows users to gain full use of their data by eliminating the silos that can complicate data. Data silos traditionally separate data engineering, analytics, BI, data science and machine learning. Companies can avoid proprietary walled gardens and other restrictions by removing these silos and allowing users to access and manage their structured and unstructured data through the Databricks platform. Users simply sync their data through a Databricks Data Lake connection for full access and automatic data update capabilities.
Snowflake supports data transformation both during loading and after it is loaded into the platform environment. The software has integration from many popular tools and solutions for easy data extraction and transformation into the target database through native connectivity with Snowflake. Snowflake takes care of multiple integration operations, including the preparation, migration, movement and management of data. In addition, the system provides capabilities for data loading from external and internal file locations, bulk loading, continuous loading and other data loading options.
Which has better data visualization?
Databricks gives users multiple methods for visualizing their data, including choropleth maps, marker maps, heatmaps, counters, pivot tables, charts, cohorts, markers, funnels, box plots, sunbursts, sankeys and word clouds. Once users store their data within their Databricks SQL data lake, they can create and save visualizations of their stored data. Users can then edit, clone, customize, or aggregate their visualizations. When they are happy with their visualizations, users can download them as image files or add them to their platform dashboards.
With the Snowflake web interface, Snowsight, users can visualize their data and query results as charts. Snowsight supports bar charts, line charts, scorecards, scatterplots and heat grids. Users can configure their data visualizations by adjusting their chart columns, column attributes and chart appearance. For example, to view data from specific time periods, users can select the buckets of time in the inspector panel to adjust the display without needing to modify their query. In addition, aggregation functions allow the system to determine single values from data points in a chart, and users can download their charts as .png files.
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Which has better data analysis?
The Databricks SQL analytics platform uses machine learning to allow users to create queries in ANSI SQL and develop visualizations and dashboards using their accessible data. The visualizations allow users to gain insights and lightweight reporting from their data lake. However, users may prefer to utilize their existing third-party BI tools by connecting them to the platform. Tools like Microsoft PowerBI or Tableau can be used for analysis and reporting directly on the Databricks data lake.
Snowflake delivers insights on data through the Snowflake Data Cloud, a data platform that can be deployed across AWS, Google and Azure. It can analyze the data for various purposes: Data Engineering, Data Science, Data Lake, Applications, and Data Sharing and Exchange. Its visualization tools can enable users to gain valuable insight and information from their data through queries. Additionally, Snowflake can be used together with other software systems for a broader range of analysis capabilities.
Which tool is a better ETL solution?
So which ETL solution is better for your organization? The best method to determine the ideal software solution for any purpose is first to identify your organization’s relevant aspects and requirements.
For example, if you require a cloud-based system for its data processing, utilizing Snowflake Data Cloud can enable your team to transform and manage its data through the online interface.
However, if your organization wishes to use its ETL solution to process big data batches, Databricks may be the better option. This is because Databricks has many functions and integrations for processing and analyzing big data sets.
Other factors to consider are the third-party products you want to use with your ETL solution. Ensure that the solution you choose has integration capabilities for each of your existing tools so that you can gain value from each of your data sources. Through thorough consideration of your organization’s needs, you can determine the best ETL solution to support your data operations.