Databricks Lakehouse Platform for all your analytics requirements
May 15, 2022
We used Databricks Lakehouse platform for running all our Machine Learning workloads as well as storing large amounts of data in our data …
Databricks Lakehouse Platform
Databricks Lakehouse Platform (Unified Analytics Platform)
Starting at $0.07 Per DBU
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$0.07
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Per DBU
Premium
$0.10
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Enterprise
$0.13
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Per DBU
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Product Details
What is Databricks Lakehouse Platform?
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle. The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations.
Databricks Lakehouse Platform Technical Details
| Deployment Types | SaaS |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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Frequently Asked Questions
What is Databricks Lakehouse Platform?
Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle. The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations.
What is Databricks Lakehouse Platform's best feature?
Reviewers rate Usability highest, with a score of 9.
Who uses Databricks Lakehouse Platform?
The most common users of Databricks Lakehouse Platform are from Enterprises (1,001+ employees) and the Information Technology & Services industry.
Reviews and Ratings
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We used Databricks Lakehouse platform for running all our Machine Learning workloads as well as storing large amounts of data in our data lake backend. The data stored in the databricks lakehouse was used to train state-of-the-art ML and Deep Learning models on text and image datasets. Databricks' Spark jobs as well as Delta Lake Lakehouse backend is well equipped for these kinds of tasks.
February 08, 2022
Best in the industry
This product is used for Data Science project development, from data analysis/wrangling to feature creation, to training, to finetuning and to model test and validation, and finally to deployment. While Databricks is used by many users, we also use GitHub and code Q/A to promote a code in production. This is one of the advantages of Databricks is the integration part, not only Git but whether you use it on Azure or AWS, you can also leverage the power of the integrated Machine Learning in those platforms, such as auto ml or Azure ML.
November 09, 2021
The wonders of all your data analysis in one place
It is currently used by our Data and Product teams in order to perform deep dives analysis on how our current metrics are performing (KPIs, OKRs), to develop tools for metric predictions based on data models in languages such as SQL and Python while mixing them and giving to the entire company visibility of the results with graphs via shared workspaces
August 13, 2021
Positive review for Databricks Lakehouse Platform
We currently use the Databricks Lakehouse Platform for a client. My team specifically uses it to data-mine, create reports and analytics for the client. Depending on where the data is stored, various Analytics teams in my company use different platforms - GCP, AWS, Databricks, etc.
August 11, 2021
My Lakehouse experiences
We build all our data pipelines with Databricks Lakehouse technology. It is reliable and the tech support from Databricks is very good.
We use Databricks to replace traditional RDBMS like Oracle. We have Big Batch ETL, Ingestion and Extraction Job for Big data ran across different products where we leverage Lakehouse platform to put our raw data in Data Lake and Create Delta Lake platform based on high performing Parquet.
It is kind of proposed to use across the whole organization and different BU's. Databricks will be our key main virtualized platform.
It addresses very fast data ingestion, reduces the overall ETL window. Integrated different datasource and also helps to perform Machine Learning jobs to run and scale. Idea is to reduce overall computation time to save cost on onprem.
It is kind of proposed to use across the whole organization and different BU's. Databricks will be our key main virtualized platform.
It addresses very fast data ingestion, reduces the overall ETL window. Integrated different datasource and also helps to perform Machine Learning jobs to run and scale. Idea is to reduce overall computation time to save cost on onprem.
July 12, 2021
Data for insights
[Databricks Lakehouse Platform (Unified Analytics Platform) is] used by a few departments to start off with data warehousing. SQL analytics, real time monitoring and data governance.
Databricks Lakehouse platform is used across all departments in my current organization.
It is used as part of solving different data engineering and data analytics use cases in different teams.
Databricks Lakehouse platform provides seamless integration with Azure cloud in Maersk. Databricks Lakehouse platform uses spark, mlops, delta for slovong the recent big data engineering problems.
It is used as part of solving different data engineering and data analytics use cases in different teams.
Databricks Lakehouse platform provides seamless integration with Azure cloud in Maersk. Databricks Lakehouse platform uses spark, mlops, delta for slovong the recent big data engineering problems.
May 28, 2021
Databricks--a good all-rounder
We use Databricks Lakehouse Platform (Unified Analytics Platform) in our ETL process (data loading) to perform transformations and to implement the toughest loading strategies on huge datasets. It is very easy to understand and it can connect to almost all the modern data formats like Avro, Parquet, and JSON. It supports almost every popular cloud platform, like Azure and AWS, and offers better performance in terms of data processing speed.
We use Databricks Lakehouse Platform to transform IoT data and build data models for BI tools. It is being used by engineering and IT teams. We use it with a data lake platform, read the raw data and transform it to a suitable format for analytics tools. We run daily/hourly jobs to create BI models and save the resulting models back to data lake or SQL tables.
January 31, 2019
Databricks for modern day ETL
Data from APIs is streamed into our One Lake environment. This one lake is S3 on AWS.
Once this raw data is on S3, we use Databricks to write Spark SQL queries and pySpark to process this data into relational tables and views.
Then those views are used by our data scientists and modelers to generate business value and use in lot of places like creating new models, creating new audit files, exports etc.
Once this raw data is on S3, we use Databricks to write Spark SQL queries and pySpark to process this data into relational tables and views.
Then those views are used by our data scientists and modelers to generate business value and use in lot of places like creating new models, creating new audit files, exports etc.
It's being used for:
- Ingestion and cleansing of data
- Interactive Analysis of data
- Development of Analytic Services
- Production Environment Customer Facing Analytic Services
August 22, 2018
Databricks Review
We leverage Databricks (DB) to run Big Data workloads. Primarily we build a Jar and attach to DB. We do not leverage the notebooks except for prototyping.
I actually use Databricks for experiments and research for my master's program. I mostly use it to implement Python codes and testing the viability of the programs that I write. Many individuals in the Computer Information System department are using this software platform to implement programs. It is a good tool for us to learn [and] includes a community forum that is rather helpful if you are self-learning and have questions.
September 15, 2017
Databricks Review
Across whole organization.
[It's] Used by self-service analysts to quickly do analysis
[It's] Used by self-service analysts to quickly do analysis