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Databricks Lakehouse Platform

Databricks Lakehouse Platform
Formerly Databricks Unified Analytics Platform

Overview

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…

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Recent Reviews

TrustRadius Insights

The Databricks Lakehouse Platform, also known as the Unified Analytics Platform, has been widely used by multiple departments to address a …
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TrustRadius Insights
TrustRadius Insights

Databricks Review

9 out of 10
August 22, 2018
Incentivized
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 …
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Awards

Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards

Reviewer Pros & Cons

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Axel Richier | TrustRadius Reviewer
Axel Richier
Tech Lead Data Engineer
Ekimetrics (Information Technology & Services, 201-500 employees)
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Pricing

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Standard

$0.07

Cloud
Per DBU

Premium

$0.10

Cloud
Per DBU

Enterprise

$0.13

Cloud
Per DBU

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting/Integration Services
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Product Details

What is Databricks Lakehouse Platform?

Databricks Lakehouse Platform Technical Details

Deployment TypesSoftware as a Service (SaaS), Cloud, or Web-Based
Operating SystemsUnspecified
Mobile ApplicationNo

Frequently Asked Questions

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.

Reviewers rate Usability highest, with a score of 9.4.

The most common users of Databricks Lakehouse Platform are from Enterprises (1,001+ employees).
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Comparisons

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Reviews and Ratings

(73)

Community Insights

TrustRadius Insights are summaries of user sentiment data from TrustRadius reviews and, when necessary, 3rd-party data sources. Have feedback on this content? Let us know!

The Databricks Lakehouse Platform, also known as the Unified Analytics Platform, has been widely used by multiple departments to address a range of data engineering and analytics challenges. Users have leveraged the platform to initiate data warehousing, SQL analytics, real-time monitoring, and data governance. The versatility and openness of the platform have allowed users to save a significant amount of time and effectively manage cloud costs and human resources.

Customers have utilized the Databricks Lakehouse Platform for various use cases, including creating dashboards with tools like Tableau, Redash, and Qlik, as well as integrating with CRM systems like Salesforce and SAP. The platform has also been employed for developing chatbots in Knowledge Management and serving machine learning models behind API endpoints. Furthermore, it is extensively used for data science project development, facilitating tasks such as data analysis, wrangling, feature creation, training, model testing, validation, and deployment.

Databricks' integration capabilities, including Git integration and integration with Azure or AWS, enable users to leverage the power of integrated machine learning features. Additionally, the platform's reliability and excellent technical support make it a preferred choice for building data pipelines and solving big data engineering problems. It is widely used by engineering and IT teams to transform IoT data, build data models for business intelligence tools, and run daily/hourly jobs to create BI models.

Moreover, the Databricks Lakehouse Platform serves as an invaluable learning tool for individuals in the Computer Information System department. The community forum proves particularly helpful for self-learners with questions. Furthermore, the platform supports deep dive analysis on metrics by Data and Product teams, facilitates client reporting and analytics through data mining capabilities, replaces traditional RDBMS like Oracle for Big Batch ETL jobs on big data sets.

In summary, the Databricks Lakehouse Platform is employed across organizations to solve a variety of data engineering and analytics use cases. Its seamless integration with cloud platforms, support for different data formats, and scalability make it suitable for tasks such as data ingestion and cleansing, interactive analysis, and development of analytic services.

User-Friendly SQL: Users have found the SQL in Databricks to be user-friendly, allowing them to easily write and execute queries. Several reviewers have praised the intuitive nature of the SQL interface, making it accessible for users of different skill levels.

Enhanced Collaboration: The enhanced collaboration between data science and data engineering teams is seen as a positive feature by many users. They appreciate how Databricks facilitates seamless communication and knowledge sharing among team members, ultimately leading to improved productivity and efficiency.

Versatile Integration: The integration with multiple Git providers and the merge assistant is highly valued by users. This feature allows for smooth version control and simplifies the collaborative development process. With this capability, developers can easily manage their codebase, track changes, resolve conflicts, and ensure a streamlined workflow.

Confusing Workspace Navigation: Several users have found the navigation to create a workspace in the Databricks Lakehouse Platform confusing and time-consuming, hindering their productivity. They have expressed frustration over the complex steps involved, resulting in wasted time.

Difficulty Locating Tables: Many reviewers have expressed difficulty in locating tables after they were created, often leading to the need for deletion and recreation. This issue has caused frustration and wasted time for users who struggle to find their data within the platform.

Random Task Failures: Some users have experienced random task failures while using the platform, making it challenging for them to debug and profile code effectively. These unexpected failures undermine confidence in the system's stability and result in delays as users attempt to identify and fix these issues.

Users highly recommend the Lakehouse platform for various data-related tasks, such as building cloud-native lakehouse platforms, ingesting and transforming big data batches/streams, and implementing medallion lakehouse architectures. They find the platform simple to use and appreciate its hassle-free administration and maintenance.

The Lakehouse platform is also highly recommended for setting up Hadoop clusters and dealing with big data, analytics, and machine learning workflows. Users believe that it provides a comprehensive and open solution for these tasks.

Users suggest exploring the features of the Lakehouse platform, such as partner connect, advanced analytics/MLOPS/Data science Auto-ML capabilities. They find these features useful and believe that they enhance the platform's salient functionalities.

Overall, users highly recommend the Lakehouse platform for its ease of use, support for major cloud providers (AWS, AZURE, GCP), and useful features like data sharing (Delta Sharing). However, users also recommend considering the level of reliance on proprietary technology versus industry standards like Spark, SQL, and dbt. It is advised to read through the documentation and gather firsthand experiences from individuals who have used the Lakehouse platform.

Attribute Ratings

Reviews

(1-17 of 17)
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July 26, 2023

Most collaborative Data Science & AI workspace !

Axel Richier | TrustRadius Reviewer
Axel Richier
Tech Lead Data Engineer
Ekimetrics (Information Technology & Services, 201-500 employees)
Score 10 out of 10
Vetted Review
Verified User
Likelihood to Recommend
Databricks shines when you are working with a growing team of multiple data professions. By providing an easy to instantiate common workspace for Data Engineers, Data Scientist, ML Engineers and Data Analyst, fully integrated with Active Directory security, it makes your data projects more likely to go to production. No need to switch between tools, to transfer the data, the Unity Catalog will centralize all the assets and all your data citizens will find it in a second and can benefit from the Spark engine whatever language they use.

It would be less appropriate for very small data projects as the entry cost may be high. Yet, if the data is meant to grow, Databricks will horizontally scale without requiring a re-write of your codebase
March 09, 2023

Databricks Lakehouse Platform: A 2-year user review

Verified User
Employee in Engineering
Retail Company, 10,001+ employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Well Suited: Dealing with big data and being able to train different models that address many problems in my business. In addition to its computational capabilities, using Databricks Lakehouse Platform allowed us to do all development in one platform. Less Appropriate: Having a small dataset that doesn't need parallel processing. Local development is easier to develop and track so if no parallelization is needed (data is not big or parallelized computations is not required), I prefer local development.
May 15, 2022

Databricks Lakehouse Platform for all your analytics requirements

Verified User
Engineer in Engineering
Computer Software Company, 1001-5000 employees
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
February 08, 2022

Best in the industry

Jonatan Bouchard | TrustRadius Reviewer
Jonatan Bouchard
Director Data Science
CN (Transportation/Trucking/Railroad, 10,001+ employees)
Score 9 out of 10
Vetted Review
Verified User
Likelihood to Recommend
Currently the best Data Science tool for a large-scale company that needs strong tech support once and a while. The performance and the connectivity/integration with a large bread of tools and platform is also important when you don't want to change all your stack. DataBricks is a great non-drage and drops tool for real Data Scientist that knows their things.
November 09, 2021

The wonders of all your data analysis in one place

Verified User
Manager in Product Management
Financial Services Company, 201-500 employees
Score 8 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
I reckon is an amazing platform for users with a certain level of expertise for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, also it is very useful when it comes to cross company shared workspaces for unified comprehension of the data.

it is less appropriate for users who don't have full knowledge of the tables they are going to query on and need more support on the data, since the platform doesn't give an option to see what are the fields in a table before even querying it
August 13, 2021

Positive review for Databricks Lakehouse Platform

Verified User
Analyst in Marketing
Marketing & Advertising Company, 5001-10,000 employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Databricks is great for beginner as well as advanced coders. The interface is extremely user-friendly and the learning curve is quite short. It is well suited for automation where we can have scripts running late at night when the load is less and wake up to an email notification of success or failure. It is also well suited for writing codes that require the use of multiple languages (in some cases of data modeling)

The ability to store temporary/permanent tables on data lakes is a fabulous feature as well. PySpark is an excellent language to learn and it works really fast with large datasets.
July 12, 2021

Data for insights

GM
Gilrod Maerina
Analyst
Riverbed Technology (Internet, 1001-5000 employees)
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
[Databricks Lakehouse Platform (Unified Analytics Platform)] makes the power of Spark accessible. Databricks's proactive and customer-centric service. It is a highly adaptable solution for data engineering, data science, and AI. Load times are not consistent and no ability to restrict data access to specific users or groups.
July 07, 2021

Databricks Lakehouse is modern solutions for current big data problems

Surendranatha Reddy Chappidi | TrustRadius Reviewer
Surendranatha Reddy Chappidi
Senior Data Engineer
A.P. Moller - Maersk (Logistics & Supply Chain, 10,001+ employees)
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Databricks Lakehouse platform is well suited for below use cases :
1. Process different types of data sources like structured data, semi structured data and unstructured data.
2. Process data different data sources like RDBMS, REST APIs, File servers, IoT sensors.
3. Provide support for Updates, Deletes, schema evaluation

Databricks Lakehouse platform is not well suited for below usecases :
1. Less data volume and doesn't have analytics requirements
2. Developers doesn't have skill set on spark and Hive


May 28, 2021

Databricks--a good all-rounder

Verified User
Professional in Information Technology
Information Technology & Services Company, 201-500 employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Databricks Lakehouse Platform (Unified Analytics Platform) can be used to process raw data from any system like IoT, structured, and unstructured data sources. Since it supports Pyspark and Scala to do data processing, it can do any complex business transformation very easily. Also, the Databricks Lakehouse Platform (Unified Analytics Platform) architecture is very similar to Big Data; it can process huge datasets from Hadoop systems and machine learning models in minutes.
May 15, 2021

Great for both ad-hoc analyzes and scheduled jobs

EO
Ender Ortak
Senior Data Analyst
Ford Otosan (Automotive, 10,001+ employees)
Score 8 out of 10
Vetted Review
Verified User
Likelihood to Recommend
It is great for both ad-hoc analyzes and scheduled jobs. It supports most of the cloud storage technologies and provides an easy to use API to connect with them. Clusters can be auto scaled with the load, and you can also create temporary clusters for job runs, which cost less compared to all purpose clusters.
January 31, 2019

Databricks for modern day ETL

Verified User
Team Lead in Engineering
Financial Services Company, 10,001+ employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.
August 22, 2018

Databricks provides a cost effective end to end solution for Enterprise analytics

Verified User
Strategist in Engineering
Computer Hardware Company, 10,001+ employees
Score 9 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Great end to end analytics solution on AWS or Azure. Databricks continues to grow based on customer feedback. Just like everyone in the industry, they are focused on Machine Learning, but they also understand a complete solution is needed.
March 28, 2018

If you want to be an effective ML learner, use Databricks

Ann Le | TrustRadius Reviewer
Ann Le
Freelance Translator
ZOO Digital Group plc (Entertainment, 501-1000 employees)
Score 7 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
Right now, I am learning about Spark ML and general machine learning concepts. It is a good practice space to run different Spark ML codes. Databricks does provide valid errors and detailed descriptions of where I can fix my code. And to run a set of codes is very easy to maneuver around. If you do not know how to code, it might be less appropriate to use Databricks. But then again, they do have a large community where help can be found.
September 15, 2017

Databricks Review

Verified User
Director in Engineering
Financial Services Company, 10,001+ employees
Score 6 out of 10
Vetted Review
Verified User
Incentivized
Likelihood to Recommend
When you have analysts that are not cloud-savvy, this tool helps them quickly run code and not be overwhelmed by infrastructure and optimization. [It's] Less appropriate in production deployments.
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