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Versatile, scalable, and adaptable framework for building a modern analytics stack
What do you like best about the product?
Databricks Lakehouse brings together BI, SQL-based data warehouses, data governance, processing and DAG creation, and ML (and more) under one umbrella. Competitors like Dataiku, Snowflake, Cloudera, etc. really can't compete and don't bring the same value proposition out of the box.
What do you dislike about the product?
Unless you're willing to keep your clusters that serve DB SQL queries spun up at all times, the "first query wait" can be quite annoying. However, using Databricks in its serverless form (managed environments) would mitigate that drawback.
What problems is the product solving and how is that benefiting you?
Streamlining data stacks and reducing the cost and complexity of deploying analytics workloads. Databricks has matured and been extended dramatically over the past few years, almost always for the good.
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Data for all.
What do you like best about the product?
Databricks is a great platform to bring all your data into a single location and provides tools for many personas to work with that data from BI to AI.
What do you dislike about the product?
There is a learning curve to using Databricks.
What problems is the product solving and how is that benefiting you?
Databricks has enabled my organization to generate insights that we previously had no visibility to.
The Lakehouse for Everything
What do you like best about the product?
For two large projects, our Big Data Analytics and Engineering teams moved to the cloud for the first time with Azure ALDS gen 2 and Databricks. We could have done the ETL the old fashioned way but decided that on a new platform we should adopt the new methodologies. We fully adopted Spark Structured Streaming Medallion Lakehouse archetecture with Bronze, Silver, Gold, and were able to deploy in just a few months what normally would have taken us a full year in Oracle with Informatica. For the first time ever our BI professionals were able to hit the same hive megastore and data model as our data scientists at blazing speeds.
What do you dislike about the product?
There isn't much that I dislike about the platform. Many of the issues that we are having have to do with using more than one workspace and the need to orchestrate jobs/workflows between them. I understand that some of that new functionality is coming in future releases.
What problems is the product solving and how is that benefiting you?
The lakehouse platform allows our Data Science, Data Management and Business Intelligence teams to be in the same environment and share data, artifacts and models created. This allows to work closer together to build solutions at the same time, instead of disjointed like in the past.
Game change in data industry. Scalable and open solutions democratizing the data access.
What do you like best about the product?
Delta lake house implementation, enabling and democratizing the data acquisition and consumption. The delta sharing initiative is so disrupting to creating data assets.
What do you dislike about the product?
It seems that we could have an easier way to suggest improvements or new features needed in the daily activities. Overall good features.
What problems is the product solving and how is that benefiting you?
Scalability and reliability on data pipelines
Efficient, user-friendly and flexible
What do you like best about the product?
All integrated platform with different tools.
What do you dislike about the product?
More visible way to manage and maintain Delta lakes
What problems is the product solving and how is that benefiting you?
Real-time transactional data storage for faster predictive maintenance.
Very excited to work using Databricks!
What do you like best about the product?
The ability to work with other professionals (data engineers and other data scientists) on the same platform. I trust Databricks and believe that they will always provide cutting-edge solutions that make Data Science projects more robust, easy and black-box-proof.
What do you dislike about the product?
Sometimes I feel a bit trapped in the platform. Solutions like Databricks-connect and DBX are great but still miss a development environment more robust than notebooks. For this reason, I tend to use virtual machine from Azure Machine Learning to develop python packages and use Databricks as a compute platform to run queries against Big Data.
What problems is the product solving and how is that benefiting you?
Developing Machine Learning models and leveraging my company's data. Other tools are more complex to use and Databricks simplify our development and our analysis.
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.
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
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.
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