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Excellent for all sorts of data analytics
What do you like best about the product?
The versatility and scalability are the best features for us. We currently use SQL, R, Python, SQL, Spark and Scala with Databricks. It's impressive how seamless this experience is for different teams with different use cases and skill sets. The interoperability across these languages and accessing data is a blessing and enables us to use a vast array of tools to solve problems.
What do you dislike about the product?
More insight into individual job costs would be a helpful feature that is currently lacking. Deploying code is also not as intuitive and the Git integration could be more powerful with an enhanced feature set.
What problems is the product solving and how is that benefiting you?
Easy integration across data sources and using a mixed bag of tools to perform advanced analytics. The mixed bag of tools available also means that we treat this as a one-stop solution and are able to serve analytics outputs to various consumers and stakeholders conveniently.
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A platform that connects the work Data Engineers, Data Analysts and Data Scientists all in one place
What do you like best about the product?
- Delta Lake & Lakehouse architecture for streaming and batch operations
- Databricks Academy provides hands-on learning and support
- Interact with Databricks resources via Terraform
- Databricks Academy provides hands-on learning and support
- Interact with Databricks resources via Terraform
What do you dislike about the product?
- Orchestration of pipelines could be improved. We currently use an external product to orchestrate our Databricks Spark jobs.
What problems is the product solving and how is that benefiting you?
- Enables data engineers to build reliable data pipelines and applications to manage their data.
- Supports ACID transactions
- New features are always being released & there is a strong Databricks community
- Supports ACID transactions
- New features are always being released & there is a strong Databricks community
Databricks is great
What do you like best about the product?
I like the simple user interface that allows me to run spark without having to do much configuration. The Terraform support is also great.
What do you dislike about the product?
Databricks runtime is not available locally to run unit tests, so some workarounds have to be made for that.
What problems is the product solving and how is that benefiting you?
Running Spark jobs on big data.
A lot easier to use than other tools I have used before.
What do you like best about the product?
Python notebooks that abstract away a lot of the complexity e.g. packages and infrastructure.
What do you dislike about the product?
The drop down menu/tool bar in the UI sometimes feels a bit clunky.
What problems is the product solving and how is that benefiting you?
Having multiple data pipelines that are able to be quickly developed and deployed, and then the output data sets made available to clients and internal teams
A great tool that simplifies your infrastructure
What do you like best about the product?
Opportunity to not manage Hadoop clusters.
What do you dislike about the product?
Cluster autoscaling doesn't always work as expected, and I would like to have more control over EC2 instances provisioning (availability to use multiple instance types in a single job/cluster, affinities, possibility to define some sort of topology, etc.). The whole experience is notebook focused.
What problems is the product solving and how is that benefiting you?
Enabling data-driven decisions by analyzing huge amounts of data
Great package for complete ML and Data engineering use cases
What do you like best about the product?
It's a complete package for development to deployment. Helps in experimentation and within a few clicks we can move it from experimentation to production.
What do you dislike about the product?
Sometimes lacks the feel of working on a traditional IDE kind of environment. However, it's not a significant drawback and one gets accustomed to it with time.
What problems is the product solving and how is that benefiting you?
Using a single platform for both data science and data engineering teams to work together and contribute. The complete journey from experimentation, modelling to deployment is seemless.
Delta Tables offers great scalable features with good performance, so low cost on cluster time.
What do you like best about the product?
Delta Tables, open source format, cloudFiles format, notebook UI and visualizations
What do you dislike about the product?
other companies do not use delta, so integration is not so simple, as delta sharing
What problems is the product solving and how is that benefiting you?
Big Data Extract Transform Load (ETL) process become very easy
Easy, fast and Reliable
What do you like best about the product?
Ease of access to multiple data sources and we can change the code to python to SQL,Scala etc it is impressive.
What do you dislike about the product?
Not able to create interactive visualization
What problems is the product solving and how is that benefiting you?
Major problem solving is of data warehousing with its multilayer data models
Excellent experience so far!!!!
What do you like best about the product?
We employ Python, Spark, and SQL to develop ELT pipelines, and Databricks is the most reliable and user-friendly option available. Developers may concentrate on writing code, creating pipelines, and creating models rather than spending time setting up the environment because it is very simple to do so.
What do you dislike about the product?
Knowledge of the cost model and recommendation engine to reduce burned DBUs There is an Overwatch notebook that offers general statistics about the environment, but it isn't developed enough and it also doesn't show you the cost of the infrastructure used in the back cloud kitchen. Platform as a whole is excellent.
What problems is the product solving and how is that benefiting you?
Two things to mention here.
- It unites all data teams from around the organization on one platform, reducing the need to maintain multiple copies of the same data.
- Because computation and storage are no longer linked, resizing any kind of warehouse environment is no longer necessary to boost compute capacity.
- It unites all data teams from around the organization on one platform, reducing the need to maintain multiple copies of the same data.
- Because computation and storage are no longer linked, resizing any kind of warehouse environment is no longer necessary to boost compute capacity.
Databricks is a very reliable way to run Spark
What do you like best about the product?
Databricks is the most reliable and flexible way to run Spark applications for data engineering workloads.
What do you dislike about the product?
Databricks is at the top end of the market on pricing.
What problems is the product solving and how is that benefiting you?
We use Databricks to run our Spark applications, which process hundreds of terabytes of data, need to be cost-effective, and run in a timely manner.
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