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One stop shop for (almost) all your analytics needs
What do you like best about the product?
The flexibility of working with notebooks that combine python and sql
What do you dislike about the product?
The visualization tools are nice but very basic and not really helpful
What problems is the product solving and how is that benefiting you?
Super fast sql engine reduces time to results from hours to seconds at a reasonable cost
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Really useful tool
What do you like best about the product?
Ease of use, really optimised platform, lots of good integrations, good customer support.
What do you dislike about the product?
The platform has some glitches that have been lying around for a while now I feel. The SQL dashboards are very very slow and the screen gets stuck often.
What problems is the product solving and how is that benefiting you?
It helps me greatly in big data analytics. The recent feature upgrades about auto-completion like VS code have been great additions. The platform is generally pretty fast.
Abstraction from non core work makes core work much easier
What do you like best about the product?
The platform allows us to quickly start developing and prototyping without worrying much about setting up workspaces, the runtimes, connectors etc. The best part is that it is really powerful to move up from basic prototyping to production ready codebase maintenance
What do you dislike about the product?
The editor could be better. I have had some poor but expected experiences with managing / writing code.
There should be better support for accessing same functionalities from CLI
There should be better support for accessing same functionalities from CLI
What problems is the product solving and how is that benefiting you?
It is giving the ability to start working immediately without worrying much about setting up / managing runtimes or workspaces. This is really helpful when you want to develop production products
My Databricks Lakehouse Platform review
What do you like best about the product?
Since Databricks Lakehouse provided a unified single platform for data processing, analysis, and machine learning, it helped me to work with the structured, semi-structured, and unstructured datas in a single environment.
What do you dislike about the product?
Databricks Lakehouse required me to learn few tools and technologies, such as Apache Spark and Delta Lake, which as a beginner was a bit complex for me to learn.
What problems is the product solving and how is that benefiting you?
Databricks Lakehouse solved the problem of data integration which involved the process of combining data from multiple sources into a single, unified format. It also offered integration with a variety of data sources, including data lakes, data warehouses, and streaming data sources thus it made easier to bring disparate data sets together in a single platform.
Overpriced and doesnt add anything we werent doing before
What do you like best about the product?
It does some compute things nicely and there are some security things in place (but not enough!)
What do you dislike about the product?
Trying to automate the infrastructure is difficult, and some things are impossible to automate as they are only available on the web console and not through APIs.
Their support is beyond bad. We had to give up working with them as it wasted too much time. Never got a solution in place. Their support is not equipped to debug problems in their own product and they do not give any alternative solutions.
I would look elsewhere for datalake solutions. There are more mature technologies out there without the limitations databricks brings. they also are less expensive and easier to deal with.
Their support is beyond bad. We had to give up working with them as it wasted too much time. Never got a solution in place. Their support is not equipped to debug problems in their own product and they do not give any alternative solutions.
I would look elsewhere for datalake solutions. There are more mature technologies out there without the limitations databricks brings. they also are less expensive and easier to deal with.
What problems is the product solving and how is that benefiting you?
ETL
One platform for all Data Management & Analytics
What do you like best about the product?
Seamless integration between spark, pyspark, scala, sparkr, and SQL APIs with Cloud Storages, Easy to use and schedule streaming and batch services with delta lake as storage for all data engineering needs with git integration and revision control.
What do you dislike about the product?
UI can be a little more like VSCode or cloud editor to give you more choices, modular code, packaged code for better unit testing, and CI CD can improve the developer experience drastically.
What problems is the product solving and how is that benefiting you?
Handling multiple tools for different data roles is an issue for many organizations. Databricks provides Data ingestion, storage, data engineering, analytics, modeling, and deployment all at one place with scale to handle petabytes of data processing using the power of spark distributed processing.
Lakehouse: Great Goals with poor execution
What do you like best about the product?
Lakehouse platform is a solution that is easy to setup, the infrastructure is easy to maintain and the UI is accessible to a wide variety of engineers.
It allows for a fast rollout to production and covers most common needs of a data company.
It allows for a fast rollout to production and covers most common needs of a data company.
What do you dislike about the product?
The biggest kink in Lakehouse platform is its speed. It does not deliver on the performance promised.
In addition, the Databricks UI is not easy to use. It feels like it's a smartphone app.
On the side of technology, it is slow and expensive, with authorization added as an afterthought.
It's an absolute pain to administer and hard to control expenses.
In addition, the Databricks UI is not easy to use. It feels like it's a smartphone app.
On the side of technology, it is slow and expensive, with authorization added as an afterthought.
It's an absolute pain to administer and hard to control expenses.
What problems is the product solving and how is that benefiting you?
We used lakehouse to ingest the events from our event based infrastructure. We produced a moderate amount of events and they all landed in the Lakehouse for analysis and additional processing.
Unified Platform & Collaborative Workspace for Data & AI/ML team
What do you like best about the product?
Databricks Serverless SQL with Photon Query acceleration for data analyst & business analyst
In-built Visualization & dashboards, along with GeoSaptial & Advanced SQL functions
Unified Pipeline for Structure streaming batch & real-time ingestion
Auto-loader for standard formats of file ingestion & Schema Evolution in-built
Delta Live Table for data Engineering Workloads & Pipelines
Databricks Multi-task Orchestration job worklfows
Unity Catalog Metstaore & its integration with other data catalogs
MLFlow for building and tracking ML experiments & Feature Store for centralized feature supply for production/inference models
Time Travel & Z-order Optimization
In-built Visualization & dashboards, along with GeoSaptial & Advanced SQL functions
Unified Pipeline for Structure streaming batch & real-time ingestion
Auto-loader for standard formats of file ingestion & Schema Evolution in-built
Delta Live Table for data Engineering Workloads & Pipelines
Databricks Multi-task Orchestration job worklfows
Unity Catalog Metstaore & its integration with other data catalogs
MLFlow for building and tracking ML experiments & Feature Store for centralized feature supply for production/inference models
Time Travel & Z-order Optimization
What do you dislike about the product?
Need to build a more comprehensive orchestration workflow JOBS panel for a diverse set of pattern design workflows
Serverless Cluster for Data Engineering Streaming/Batch pipelines
Integrate most IDE features into the notebook
Clear documentation on Custom Databricks runtime docker image creation will be helpful
Lineage & flow monitoring dashboard can be built automated for non-DLT jobs as well
DLT implementation can be extended to other DELTA format supporting warehouse in future
Serverless Cluster for Data Engineering Streaming/Batch pipelines
Integrate most IDE features into the notebook
Clear documentation on Custom Databricks runtime docker image creation will be helpful
Lineage & flow monitoring dashboard can be built automated for non-DLT jobs as well
DLT implementation can be extended to other DELTA format supporting warehouse in future
What problems is the product solving and how is that benefiting you?
Unified Pipeline for Structure streaming batch & real-time ingestion
The schema merge feature helps to track the change in Schema
DLT feature helps to build Data Quality Lineage along with automated Pipeline links to the reference LIVE tables
Auto-loader helps to build the common ingestion framework for our enterprise
The schema merge feature helps to track the change in Schema
DLT feature helps to build Data Quality Lineage along with automated Pipeline links to the reference LIVE tables
Auto-loader helps to build the common ingestion framework for our enterprise
Solid Data Platform
What do you like best about the product?
Fast iterative abilities and notebook baseed UI. It is also helpful to have multiple contributors on a single notebook at one time. You can see where others are in the notebook which helps with collaboration.
What do you dislike about the product?
The UI frequently exhibits unintended behavior. I will occasionally have random characters added to random cells in the notebook causing errors. It makes debugging difficult when you made no changes and a working cell is now causing errors.
What problems is the product solving and how is that benefiting you?
We are using Databricks to move large amounts of data. Our team is able to run different ETL pipelines with different schedules in an organized way. We are able to quickly iterate on our notebooks to add new features.
Great platform for working collaboratively
What do you like best about the product?
- Ability to edit the same notebook with collaborators
- GitLab compatibility
- Multiple languages supported
- Broad functionality allows most of our digital teams to use it for their own needs
- Spark compute is fast and the amount of processors on a cluster is clear
- GitLab compatibility
- Multiple languages supported
- Broad functionality allows most of our digital teams to use it for their own needs
- Spark compute is fast and the amount of processors on a cluster is clear
What do you dislike about the product?
- UI is constantly changing, and changes are not announced with any leadup
- UI can be buggy - WebSocket disconnects, login timeouts, copy/pasting into incorrect cells
- Pricing structure is a little opaque - DBUs don't have a clear dollar-to-time amount
- Notebook structure isn't perfect for production engineering, better for ML or ad-hoc operations
- UI can be buggy - WebSocket disconnects, login timeouts, copy/pasting into incorrect cells
- Pricing structure is a little opaque - DBUs don't have a clear dollar-to-time amount
- Notebook structure isn't perfect for production engineering, better for ML or ad-hoc operations
What problems is the product solving and how is that benefiting you?
- Maintains access to all of our business data on both AWS and Azure, and can switch between those platforms
- Has an interface for data scientists, engineers, and business users and prevents needing to buy additional tools
- Allows big data applications to run without having to do much Spark configuration
- Has an interface for data scientists, engineers, and business users and prevents needing to buy additional tools
- Allows big data applications to run without having to do much Spark configuration
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