Databricks Data Intelligence Platform
Databricks, Inc.External reviews
640 reviews
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I am using databricks on my daily routine , experienced a wonderful experience
What do you like best about the product?
I like delta live table the most because of its working and the exposure it gave to the customer like data constraints and data quality check , that is best
What do you dislike about the product?
I dislike the python syntax and code to create the delta live tables , so confusing and need to be change the logic , sql syntax is best
What problems is the product solving and how is that benefiting you?
As Delta live tables comes into picture , we dont have to focused on the data quality just only focus is to read the file and all the work will be done by delta live tables pipelines
Swiss-Army Knife of Data Analytics
What do you like best about the product?
Databricks' versatility is its best feature. The range of languages and functionality afforded by Databricks is impressive. Thus far, I've written code in R, Python, SQL and Scala in Databricks. And im just getting started. But I've composed SQL code in both R and Python, executed in Databricks. And then we come to interoperability. Data written to SQL can be accessed by either R or Python. Parameters can be passed across SQL, R and Python via widgets or environmental variables. If you have an intractable data or analytics problem, Databricks would be my 'go to' to maximise the options as to how you could potentially code your way under, around or over the obstacles standing between your project and successful execution.
What do you dislike about the product?
The options for deployment of Databricks code from dev >> qa >> uat >> prod aren't as intuitive as I might like. This might have more to do with our current use of Azure Data Factory for orchestration. Setting up workflow natively in Databricks was quite straightforward. It seems to be accessing Databricks notebooks from Azure Data Factory in dev >> qa >> uat >> prod where we are perhaps creating problems for ourselves. Perhaps not a shortcoming in Databricks at all. Curious as to how Databricks would operate with AWS rather than Azure. Perhaps a better experience?
What problems is the product solving and how is that benefiting you?
Data migration, data modelling & reporting.
Best Data Engineering, ML, Data Science & analytics lakehouse platform
What do you like best about the product?
Autoloader
Change Data Feed
DLT pipelines
Schema evolution
Jobs Multitask
Integration with leading Git Providers, Data Governance and security tools
MLflow AutoML
Serverless SQL endpoints for analyst
Photon accelerated engine
Change Data Feed
DLT pipelines
Schema evolution
Jobs Multitask
Integration with leading Git Providers, Data Governance and security tools
MLflow AutoML
Serverless SQL endpoints for analyst
Photon accelerated engine
What do you dislike about the product?
No GUI-based drag & drop
Complete Data Lineage visualization at the metadata level is still no there
NO serverless Cluster for data engineering pipelines if you use existing interactive clusters, only available through job clusters through DLT
Every feature has some limitations involved
More work is needed on orchestration workflows
Complete Data Lineage visualization at the metadata level is still no there
NO serverless Cluster for data engineering pipelines if you use existing interactive clusters, only available through job clusters through DLT
Every feature has some limitations involved
More work is needed on orchestration workflows
What problems is the product solving and how is that benefiting you?
Unified Batch & streaming pipeline
Delta Lake
Versioning & History
ACID transaction through delta log
Data curation through Validation & quarantine
Data Ingestion through Autoloader
Delta Lake
Versioning & History
ACID transaction through delta log
Data curation through Validation & quarantine
Data Ingestion through Autoloader
Databricks - Best Unified Delta Lakehouse Platform in Data & AI Analytics space
What do you like best about the product?
Unified Batch & Streaming for source systems data
Autoloader capability, along with Schema Evolution
Delta Live Table & orchestrating with Pipelines
CDC Event streams for SCD1 & SCD2 using DELTA apply changes
Databricks Workflows - Multi-task jobs
Serverless SQL Photon cluster along with Re-dash integrated Visualization
Unity Catalog
Delta Sharing & Data MarketPlace
Data Quality expectations
Integration with Collibra, Privacera & other security & governance tools
Autoloader capability, along with Schema Evolution
Delta Live Table & orchestrating with Pipelines
CDC Event streams for SCD1 & SCD2 using DELTA apply changes
Databricks Workflows - Multi-task jobs
Serverless SQL Photon cluster along with Re-dash integrated Visualization
Unity Catalog
Delta Sharing & Data MarketPlace
Data Quality expectations
Integration with Collibra, Privacera & other security & governance tools
What do you dislike about the product?
There is an issue with running multiple streaming jobs on the same cluster. Job clusters cannot be reused even for the same retry in PRODUCTION, as they are set to shut down immediately after the job runs or fails by default. We need to check for any options to increase this limit.
Multi-Task jobs require that the output of one TASK be passed as the input to the next TASK. Additionally, they need to support triggering a FAIL and setting OR-dependent predecessors to trigger, but currently, only AND is supported.
There is no serverless option for Data Engineering jobs outside of DLT (Delta Live Tables).
DLT needs to mature to handle a wide variety of integrating sources and targets, as it currently only supports DELTA tables in Databricks. It is expected to support any tool/service/product that supports DELTA format filesystems.
Multi-Task jobs require that the output of one TASK be passed as the input to the next TASK. Additionally, they need to support triggering a FAIL and setting OR-dependent predecessors to trigger, but currently, only AND is supported.
There is no serverless option for Data Engineering jobs outside of DLT (Delta Live Tables).
DLT needs to mature to handle a wide variety of integrating sources and targets, as it currently only supports DELTA tables in Databricks. It is expected to support any tool/service/product that supports DELTA format filesystems.
What problems is the product solving and how is that benefiting you?
Easier integration from various source systems starting from IoT, streaming connectors, and batch connectors.
Helpful to easily design the lakehouse medallion architecture: RAW, REFINED, and GOLD to contextualize the enterprise common data model and warehouse systems.
Data quality expectations in DLT are very helpful to speed up the quality check process and display in the monitoring dashboard lineage process.
Auto-tuning - compaction is helpful along with VACUUM.
Able to integrate metastore well with Collibra for data governance.
Helpful to easily design the lakehouse medallion architecture: RAW, REFINED, and GOLD to contextualize the enterprise common data model and warehouse systems.
Data quality expectations in DLT are very helpful to speed up the quality check process and display in the monitoring dashboard lineage process.
Auto-tuning - compaction is helpful along with VACUUM.
Able to integrate metastore well with Collibra for data governance.
Scalable, fast and easy to use
What do you like best about the product?
Databricks delta lake is the default storage for databricks which makes it very useful. Time travel, transaction, partitioning makes it very efficient.
What do you dislike about the product?
Until now I have not faced any limitations for my use case.
What problems is the product solving and how is that benefiting you?
We are using databricks lakehouse to manage batch and realtime data pipeline to fetch data from elasticsearch & Azure datalake.
Very friendly with Jupyter users.
What do you like best about the product?
Data Engineer & Machine Learning is very easy to use.
What do you dislike about the product?
It takes time to start a job cluster so I must create a cluster for live update dashboard.
What problems is the product solving and how is that benefiting you?
Schedule ETL jobs.
Many teams can use with different programming languages.
Many teams can use with different programming languages.
Databricks lakehouse
What do you like best about the product?
Everything is on a single platform like ETL, Sql dashboard and running ML models.
Simplied version for creating scheduled jobs using workshops and the best part is Delta Lake.
Simplied version for creating scheduled jobs using workshops and the best part is Delta Lake.
What do you dislike about the product?
Every piece of code should be in the form of notebooks which sometimes makes it difficult to manage. It can be more user friendly if they give different options.
What problems is the product solving and how is that benefiting you?
Time travel feature allows to version database instead of keeping redundant or replicas. This has optimized both in terms of human efforts and cost.
Lakehouse is the Marriage of cloud based DW and Data Lake
What do you like best about the product?
It is a cloud native modern data estate service which handles core DW concepts around competing with snowflake and delta live table schema requirements like CDC like a champ
What do you dislike about the product?
Lakehouses are great but not the answer to everything when it comes to all the needs of cloud scale analytics and AI
What problems is the product solving and how is that benefiting you?
Lakehouse model enables you to work in a truly unified architectyou are that provides highly performant and structured streaming capabilities, and most importantly for me, data science, machine learning and Visualization capabilities.
Databricks Lakehouse is one shop stop any bigdata analytics
What do you like best about the product?
Databricks lakehouse is one shop stop for analytics with Big data case
What do you dislike about the product?
Databricks have many releases one going and that might create a need for customer to constantly updates there infrastructure
What problems is the product solving and how is that benefiting you?
It helps optimise Spark engine by building a wrapper compute on Spark cluster and this help run huge volume based queries much faster
One Stop Shop for Data Engineers
What do you like best about the product?
One platform to access Notebooks, tables, AI/Ml Platform
What do you dislike about the product?
No debugger like other IDE's, difficult to navigate notebooks and functions
What problems is the product solving and how is that benefiting you?
Formed Datalake to reduce efforts and time for internal teams
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