What is Databricks?
Databricks claims to be a cloud data platform that integrates the whole data ecosystem end to end. Their purpose is to enable organizations to equip their employees, customers, operational workflows, products, and services with timely and trusted data. Originally designed for data engineers, Databricks has expanded to focus on self-service capabilities for Data Scientists.
What is Datagran?
Datagran is an integrated, self-service cloud data platform that enables data teams and business teams to work better together - streamlining the path from data to decisions. Our mission is to speed up the process of data workflows without the need of engineering resources, grow data adoption and company-wide trust in data by empowering business teams to access and analyze data, whenever and however they need it. Our robust modeling layer is also user-friendly enough for data-savvy business users to participate.
A good cloud data platform should be accessible and beneficial to everyone. These factors are vital for business teams to make informed, timely decisions. As such, things like learning curve, lengthy training time, implementation time, slow performance and load time, and limitations for non-technical business users are red flags.
While Databricks is a robust, powerful cloud data solution that equally plays in the self-service space, there are certain downsides that negatively impact the ongoing accessibility of data for all users. In this comparison, we’re considering the perspective of a large tech and data saavy company that’s mature in their data journey, with teams embedded in business units. The main downsides include data engineering resources, slowness, overall learning curve and complexity, and cost.
Datagran, on the other hand, is fast to implement, learn, and load, doesn’t require any engineering resources. Plus, we’re significantly cheaper with free viewer licenses.
|Pricing||Pricing is usage based and meassured via a propietary metric called DUs. Pricing of DUs varie per product. For example Databricks Notebook, Serverless SQL and Jobs are each priced separatly. For example, a customer with 100 jobs at $0.10 per DBU when each job costs aroun 250 DBUs might end up paying around $10k per month only on job related pricing.||Pricing is usage based and meassured via a propietary metric called Data Units. Datagran does not have differential pricing between products. Each Data Unit is $1 dollar and translates into one hoy of machine time. On average our enterprise clients spend around $300 in Data Units per month which translates in 300 hours of machine time.|
|Other than that all of our plans start at free forever and Enterprise at $5,000 per month and include 150 data units, 5 users and 50 million data rows.|
|Learning curve||Steep - must have Data Engineers to setup infraestructure. Heavy coding requirements in modeling, exploration, and iteration.||Easy - Datagran provides code and low-code tools. It even provides an AI code assistant that understands plain english. No engineering required.|
|Average roll-out time||6-12 months.||2 weeks.|
|Data connectors||Not included||Included|
|Destinations||Not included||Provides multiples destinations like for example Intercom, Salesforce, Snowflake and many more.|
|Speed||Databricks Notebooks runs with all the customers data which makes the process slow.||Datagran is optimized for big data offering several options for working with sample data sets.|
|Iterface||In-browser; no desktop install. Mobile version is limited.||In-browser; no desktop install. Mobile version is limited.|
|Software updates||Updates are completed per customer and not globally for all customers, which can be a pain when migrating your deployment.||Datagran updates all customer deployments at all times and makes sure nothing is broken and no one is left behind.|
|User permission management||Yes||Yes|
|Modeling layer||Jupyter notebook like coding environment running specifically on Spark.||Hosted VSCode IDE that runs with or without Spark. Datagran also provides multiple low code tools as well as an AI code assistant.|
|Customer Service||Expect long delays in terms of responsiveness and resolution||Highly reactive with SLAs in place; quick problem-solving|