Find out what your peers are saying about Microsoft, Salesforce, Domo and others in Reporting.
When it comes to big data processing, I prefer Databricks over other solutions.
For a lot of different tasks, including machine learning, it is a nice solution.
Whenever we reach out, they respond promptly.
As of now, we are raising issues and they are providing solutions without any problems.
I rate the technical support as fine because they have levels of technical support available, especially partners who get really good support from Databricks on new features.
The patches have sometimes caused issues leading to our jobs being paused for about six hours.
I would rate the scalability of this solution as very high, about nine out of ten.
Databricks is an easily scalable platform.
They release patches that sometimes break our code.
Databricks is definitely a very stable product and reliable.
Although it is too early to definitively state the platform's stability, we have not encountered any issues so far.
We use MLflow for managing MLOps, however, further improvement would be beneficial, especially for large language models and related tools.
They're now coming up with their IBI dashboard, and I think they're on the right track to improve that even further.
It would be beneficial to have utilities where code snippets are readily available.
It is not a cheap solution.
The Unity Catalog is for data governance, and the Delta Lake is to build the lakehouse.
Databricks' capability to process data in parallel enhances data processing speed.
The platform allows us to leverage cloud advantages effectively, enhancing our AI and ML projects.
| Product | Market Share (%) |
|---|---|
| Birst | 0.7% |
| Microsoft Power BI | 18.8% |
| Tableau Enterprise | 16.8% |
| Other | 63.7% |
| Product | Market Share (%) |
|---|---|
| Databricks | 9.2% |
| Snowflake | 16.1% |
| Teradata | 8.5% |
| Other | 66.2% |
| Company Size | Count |
|---|---|
| Small Business | 3 |
| Midsize Enterprise | 7 |
| Large Enterprise | 12 |
| Company Size | Count |
|---|---|
| Small Business | 25 |
| Midsize Enterprise | 12 |
| Large Enterprise | 56 |
Birst Networked BI and Analytics eliminates information silos. Decentralized users can augment the enterprise data model virtually, as opposed to physically, without compromising data governance.
A unified semantic layer maintains common definitions and key metrics.
Birst’s two-tier architecture aligns back-end sources with line-of-business or local data. Birst’s Automated Data Refinement extracts data from any source into a unified semantic layer. Users are enabled with self-service analytics through executive dashboards, reporting, visual discovery, mobile tools, and predictive analytics. Birst Open Client Interface also offers integration with Tableau, Excel and R.
Birst goes to market in two primary ways: as a direct sale, for enterprises using Birst on internal data to manage their business; and embedded, for companies who offer analytic products, by embedding and white-labeling Birst capabilities into their products.
Birst’s is packaged in 3 available formats: Platform and per-user fee; by Department or Business Unit; by end-customer (for embedded scenarios).
Databricks offers a scalable, versatile platform that integrates seamlessly with Spark and multiple languages, supporting data engineering, machine learning, and analytics in a unified environment.
Databricks stands out for its scalability, ease of use, and powerful integration with Spark, multiple languages, and leading cloud services like Azure and AWS. It provides tools such as the Notebook for collaboration, Delta Lake for efficient data management, and Unity Catalog for data governance. While enhancing data engineering and machine learning workflows, it faces challenges in visualization and third-party integration, with pricing and user interface navigation being common concerns. Despite needing improvements in connectivity and documentation, it remains popular for tasks like real-time processing and data pipeline management.
What features make Databricks unique?
What benefits can users expect from Databricks?
In the tech industry, Databricks empowers teams to perform comprehensive data analytics, enabling them to conduct extensive ETL operations, run predictive modeling, and prepare data for SparkML. In retail, it supports real-time data processing and batch streaming, aiding in better decision-making. Enterprises across sectors leverage its capabilities for creating secure APIs and managing data lakes effectively.
We monitor all Reporting reviews to prevent fraudulent reviews and keep review quality high. We do not post reviews by company employees or direct competitors. We validate each review for authenticity via cross-reference with LinkedIn, and personal follow-up with the reviewer when necessary.
