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 solution is a necessity.
This resource will analyze two of the top ETL tools, Databricks and Snowflake, so you can see which would better satisfy your data extraction, transformation and loading needs.
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: Comparison table
|Focus on data warehousing
|Real-time data analytics
|Built-in machine learning
Databricks and Snowflake pricing
After a free trial, Databricks can be purchased as a pay-as-you-go solution, with pricing based on computer usage. Alternatively, customers can purchase the software through a committed use plan. This means that users can commit to certain levels of usage and gain discounts when purchasing the software.
Snowflake offers similar pricing models for its software. The Data Cloud service can be purchased through a pay-as-you-go model that is usage-based with no long-term commitment, or through Snowflake On Demand. This lets customers access Snowflake by choosing pre-purchased software capacity options and promises discounts on the software’s overall cost.
Databricks vs. Snowflake feature comparison
Integration and synchronization
The Databricks solution allows users to make 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 with 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.
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.
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.
Databricks pros and cons
Pros of Databricks
- Built-in machine-learning capabilities.
- Helpful online guides for utilizing and navigating the software.
- Support for R, Java and Python.
Cons of Databricks
- Steep learning curve for new users.
- Challenging initial installation.
Snowflake pros and cons
Pros of Snowflake
- Superb for data warehousing needs.
- User-friendly interface with automatic performance scaling.
- Useful integrations for extending the functionality of Snowflake’s software.
Cons of Snowflake
- No built-in support for machine learning.
- Provides limited control over the infrastructure.
This is a technical review using compiled literature researched from relevant databases. The information provided within this article is gathered from vendor websites or based on an aggregate of user feedback to ensure a high-quality review.
Should your organization use Databricks or Snowflake?
So which ETL solution is better for your organization? The best method to determine the ideal software solution for any purpose is to first 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.
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This post originally appeared on TechToday.