Databricks Lakehouse Platform for all your analytics requirements
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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$0.10
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$0.13
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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.
- Very well optimized Spark Jobs Execution Engine.
- Time travel in Databricks Lakehouse Platform allows you to version your datasets.
- Newly integrated Analytics feature allows you to build visualization dashboards.
- Native integration with managed MLflow service.
- Running MLflow jobs remotely is extremely cluttered and needs to be simplified.
- All the runnable code has to stay in Notebooks which are not very production-friendly.
- File management on DBFS can be improved.
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.
- Data Science code agnostic (SQL, R, Pyton, Pyspark, Scala)
- Customer Service with REAL support from data eng. and data scientist
- Integration with many technology : Tableau, Azure, AWS, Spark, etc.
- Visualization
- Collaboration
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
- Cross company shared workspaces for unified comprehension of the data
- Combining different languages such as SQL and Python in one single space in order to make data analysis
- Quick execution of highly complex queries
- How graphs are created, it requires a certain level of expertise in the platform and it could be more intuitive and user friendly
- More guidance on the basics, since some of the new users come from different platforms expecting a similar UI
- An option where all the tables are shown with their respective fields, when a DB is selected for a query
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.
- Scheduling jobs to automate queries
- User friendly - a new user can easily navigate through SQL/Python queries
- Options to code in multiple languages (SQL, Python, Scala, R) and easy to switch with the use of the % operator
- Errors can be difficult to understand at times
- Session resets automatically at times, which leads to the temporary tables being wiped out from memory
- Git connections are dicey
- Very inconsistent with job success/failure notification emails
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.
- Better performance through consolidating small files in delta tables
- ACID functionality on delta tables
- Live delta tables
- Make it easier to test features in public preview, like delta live tables.
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.
- Data Virtualization
- Spark Real time and Batch streaming
- Notebook to run Jobs
- integrate Python and Apache Spark SQL
- SQL Analytics
- SQL Analytics Performance
- Help migration for RDBMS sources
- To make Transactional OLTP aspects faster
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.
- SQL
- User friendly
- Great development environment
- Errors are not explained
- No data back up feature
- Interface can be more intuitive
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.
- Seamless integration with Azure cloud platform services like Azure Data Lake Storage, Blobstorage , Azure Data Factory, Azure DevOps.
- Databricks lakehouse platform in backed uses Apache Spark for all the computation to be faster and distributed. It helps to complete data pipelines to process huge amounts [of] big data in lesser time with low cost.
- Databricks Lakehouse solves the problems data lake, by introducing Delta Lake concept. It provides support for updates, deletes, schema evaluation.
- Databricks Lakehouse platform can provide better platform for managing, and monitoring the cluster performance, utilization, optimization suggestions. It helps developers to leverage those insights for building better data pipelines.
- Databricks Lakehouse platform can provide GUI version to create spark jobs by click, drag and drop. That reduces the significant amount of time to develop code.
- Databricks Lakehouse platform can provide better insights and details regarding the jobs failures and resources consumption
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.
- Complex transformations
- Supports major data sources
- Great performance
- User interface to connect data sources
- Pricing
- Community support
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.
- Ready-2-use Spark environment with zero configuration required
- Interactive analysis with notebook-style coding
- Variety of language options (R, Scala, Python, SQL, Java)
- Scheduled jobs
- Random task failures
- Hard to debug code
- Hard to profile code
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.
- Process raw data in One Lake (S3) env to relational tables and views
- Share notebooks with our business analysts so that they can use the queries and generate value out of the data
- Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
- Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
- Databricks should come with a fine grained access control mechanism. If I have tables or views created then access mechanism should be able to restrict access to certain tables or columns based on the logged in user
- There should be improved graphing and dash boarding provided from within Databricks
- Better integration with AWS could help me code jobs in Databricks and run them in AWS EMR more easily using better devops pipelines
It's being used for:
- Ingestion and cleansing of data
- Interactive Analysis of data
- Development of Analytic Services
- Production Environment Customer Facing Analytic Services
- Collaborative Development Environment using Notebooks.
- Stable and Secure Cloud Development Environment requiring minimum DevOPs support
- Fast with excellent scalability reduces time to market
- Open source library support
- Automation of Machine Learning Development
- Optimization of GPU usage
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.
- Extremely Flexible in Data Scenarios
- Fantastic Performance
- DB is always updating the system so we can have latest features.
- Better Localized Testing
- When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
- Graphing Support went non-existent; when it was one of their compelling general engine.
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.
- There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
- There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.
- The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
- Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.
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
- Very simplified infrastructure initialization
- Seamless and automated optimization of job execution
- Simple tool to get used to
- Visualization - Great area of improvement
- Integration with Git
- COST