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Changes due to Lakehouse usage
What do you like best about the product?
Databricks Data Science and Engineering Workspace allows writing the coding in various languages and it enables the ingestion process simpler and guarantee that data available for business queries are reliable and current
What do you dislike about the product?
Reusing the Cluster feature and delta live tables features was the least liked process, due to the missing link to the GIT integration directly from the Repos.
If this is available then we will be able to use these cool features widely
If this is available then we will be able to use these cool features widely
What problems is the product solving and how is that benefiting you?
Databricks Lakehouse platform has been used to resolve the common Big Data and the AI problems and helped our organization to utilize the cool compute features of Databricks
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Databricks Rocks!
What do you like best about the product?
combines data warehousing with data lakes; ease of use and implementation; compatibility with BI tools
What do you dislike about the product?
nothing i can think of - databricks is awesome
What problems is the product solving and how is that benefiting you?
it helps with business intelligence and analytics and is easily used with our techstack
Databricks
What do you like best about the product?
A single unified platform that can be used for both real-time and batch data ingestion patterns to fulfill both BI and advanced analytics use cases.
What do you dislike about the product?
Nothing. I absolutely love the platform.
What problems is the product solving and how is that benefiting you?
is helping us build a Unified Data Platform.
Great platform for collaboration and data analysis
What do you like best about the product?
Integration with Github repos and CI/CD pipelines. Also having different ways to collaborate with team members and stakeholders (repos, workspace, Databricks SQL)
What do you dislike about the product?
Depending on cluster settings and number of users running queries at the same time and the number of jobs running at the same time, it can sometimes take time to run queries
What problems is the product solving and how is that benefiting you?
Having a single place to store data and being able to merge it and analyze it together is useful to enable insights for the decision-making roles as well as operations teams
Best Lakehouse Platform for building enterprise data pipelines for business needs
What do you like best about the product?
No 1 - Delta Lakehouse platform supports ACID transactions (Data lake + Datawarehouse)
Easy DLT pipeline with lineage & quality
Unified governance with the unity catalog
Support Schema evolution
Exceptional AUTOLOADER capability
Easy DLT pipeline with lineage & quality
Unified governance with the unity catalog
Support Schema evolution
Exceptional AUTOLOADER capability
What do you dislike about the product?
Awaiting for the Serverless Data engineering pipeline with NO capacity planning outside DLT with SLA-based scaling ( I know it's on ROADMAP, I am waiting).
More features on GCP+Databricks integration compared to same as AWS, Azure. (Some capabilities like credential passthrough missing in GCP)
More features on GCP+Databricks integration compared to same as AWS, Azure. (Some capabilities like credential passthrough missing in GCP)
What problems is the product solving and how is that benefiting you?
Data Lake + Datawarehousing (Unifies Lakehouse Platform)
Delta lake capabilities
Schema evolution
Data quarantine & Data Quality
Data Integration & Transformations
Delta lake capabilities
Schema evolution
Data quarantine & Data Quality
Data Integration & Transformations
Recommendations to others considering the product:
Kindly go for this for building a cloud-native lakehouse platform for big data batch/streaming ingestion, quality, transformations and building the medallion lakehouse architecture (unified data lake + Datawarehouse) data mesh experience for end consumers. Best in the market which supports AWS,AZURE and GCP cloud.
Partner Connect, Advanced analytics/MLOPS/Data science Auto-ML also looks good with improving salient features.Go for this product which combines all in one suite
Data Sharing (Delta Sharing) is quite useful for security/compliance
Partner Connect, Advanced analytics/MLOPS/Data science Auto-ML also looks good with improving salient features.Go for this product which combines all in one suite
Data Sharing (Delta Sharing) is quite useful for security/compliance
easy to use platform for large scale data ETL and analytics
What do you like best about the product?
Has tools like AutoML which reduces human effort and increases better predictions and deeper understanding of the data
What do you dislike about the product?
The platform can be slow sometimes. Other than that not major issues worth mentioning
What problems is the product solving and how is that benefiting you?
Analysis and Analytics. Use case - Labour market research
The best platform for building the future
What do you like best about the product?
1. The core storage technology is Open Source (Delta Lake)
2. Multiple data formats fully accessible via Spark/Python or SQL
3. Ability to manage code via our own GitHub repositories
2. Multiple data formats fully accessible via Spark/Python or SQL
3. Ability to manage code via our own GitHub repositories
What do you dislike about the product?
1. Not always obvious which pieces are (or will be) open source vs proprietary
2. GitHub integration doesn't support multiple branches, making it difficult to develop alongside production
3. Hard mode-switch between SQL and Data Science user interfaces feels needlessly complex (though I understand there is some technical justification for it)
2. GitHub integration doesn't support multiple branches, making it difficult to develop alongside production
3. Hard mode-switch between SQL and Data Science user interfaces feels needlessly complex (though I understand there is some technical justification for it)
What problems is the product solving and how is that benefiting you?
1. THE single source of truth for all our enterprise data, including Salesforce and NetSuite
2. Straightforward integration of our business data with our IoT product data
3. Elegant console (using Quilt Data Smart Reports) for presenting that data to multiple stakeholders
2. Straightforward integration of our business data with our IoT product data
3. Elegant console (using Quilt Data Smart Reports) for presenting that data to multiple stakeholders
Recommendations to others considering the product:
Decide up front how much you want to take advantage of their proprietary technology (such as live tables), versus industry standards such as Spark, SQL, and dbt. There's no right answer, but the more mindful you are about those tradeoffs the fewer regrets you will have down the road.
Going in the right direction but might take a while. Best platform to bet on
What do you like best about the product?
Easy to use and and very small learning curve. This makes it easy to start focusing on the actual probelm statement and start getting value out of it.
What do you dislike about the product?
UX. Though there are features available, sometime it's hard to find. If you're not trained, your eye might not catch it. Some features can only be applied via API. This require to keep a constant watch in the documentation to know what other options available. At least those options could be provided as a note in the UI for knowing there are other possibilities.
What problems is the product solving and how is that benefiting you?
We are building a data platform using lakehouse to empower the whole organisation to take data driven decision. Databricks providing us a platform to move fast without thinking much about infrastructure. We could easily scale. At the same time it is almost like open source. There is very little vendor lock-in risk.
Recommendations to others considering the product:
Use it to move fast! It cost a bit on the higher side. As it is built on top of open source, there are plenty of options to move out at a later point when you're mature if you are worried too much about the cost or just continue using it if you don't want the management overhead and the addon performance benifits.
Great Experience All Around
What do you like best about the product?
A great experience that combines ML-Runtimes - MLFlow and Spark. The ability to use Python, and SQL seamlessly in one platform. Since databricks notebooks can be saved as python scripts in the background it is amazing to have both notebook and script experience and synchronize to git.
What do you dislike about the product?
Debugging code and using interactive applications outside out databricks approved tools can be tricky. It is hard to get a grasp of the documentation for beginners to the platform.
What problems is the product solving and how is that benefiting you?
Highly scalable data pipelines with machine learning tools. Geospatial analyses. The scalability of the platform really increased our efficiency and reaction speed to customer requirements.
Lakehouse made simple
What do you like best about the product?
For me, I like the data science and SQL platform best. They are extremely helpful for my job, allowing me to streamline my work and automate it using Jobs.
What do you dislike about the product?
Sometimes the platform can be a bit slow to react but I'm not sure if it's the cluster size or something is wrong with Databricks itself. Overall I didn't find many issues with the platform.
What problems is the product solving and how is that benefiting you?
I'm solving automation issues along with ETL work. I'm able to use Databricks with our datalake in the easiest possible way, using different data formats with ease.
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