Databricks Data Intelligence Platform Reviews and Ratings

Use Cases and Deployment Scope

Databricks is the primary data platform where we land, standardize, clean, transform, and clean our data sources. We utilize the Workflows feature to automate reoccurring tasks and have built internal applications around the reusable workflows. We use the dashboard feature internally to allow customer success teams and business analysts to keep tabs on the performance and outputs of our products. The workloads are orchestrated in Databricks but executed within our own AWS accounts, allowing us to stay compliant with our stringent security requirements.

Likelihood to Recommend

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

Use Cases and Deployment Scope

I use Databricks Lakehouse Platform in my Data Scienc & AI consulting company to help various business entities with data-driven solutions. The platform can handle large and complex data sets and enable us to build and deploy applications using the latest technologies. The opennness of Databricks allows us to seamlessly integrate and adapt to our clients requirements :

* Creating dashboards with Tableau, Redash, Qlik,

* Feed their CRM tool like Salesforce, SAP,

* developing chatbots for Knowledge Management

* Serve ML models behind API endpoints.

Databricks Lakehouse Platform is a versatile and open product that saves us a lot of time, help us control cloud cost and human resources energy !

Likelihood to Recommend

Databricks shines when you are working with a growing team of multiple data professions. By providing an easy to instantiate common workspace for Data Engineers, Data Scientist, ML Engineers and Data Analyst, fully integrated with Active Directory security, it makes your data projects more likely to go to production. No need to switch between tools, to transfer the data, the Unity Catalog will centralize all the assets and all your data citizens will find it in a second and can benefit from the Spark engine whatever language they use.

It would be less appropriate for very small data projects as the entry cost may be high. Yet, if the data is meant to grow, Databricks will horizontally scale without requiring a re-write of your codebase

Use Cases and Deployment Scope

I use Databricks Lakehouse Platform to build a data-science based solutions that adress many problems in my business. This includes: increment our data in the lake house and use Databricks Lakehouse Platform computational capabilities to analyze and feature engineer our data, build different machine learning model and track different experiment and finally register our trained model that can be used by the business.

Likelihood to Recommend

Well Suited: Dealing with big data and being able to train different models that address many problems in my business. In addition to its computational capabilities, using Databricks Lakehouse Platform allowed us to do all development in one platform. Less Appropriate: Having a small dataset that doesn't need parallel processing. Local development is easier to develop and track so if no parallelization is needed (data is not big or parallelized computations is not required), I prefer local development.

Use Cases and Deployment Scope

We used Databricks Lakehouse platform for running all our Machine Learning workloads as well as storing large amounts of data in our data lake backend. The data stored in the databricks lakehouse was used to train state-of-the-art ML and Deep Learning models on text and image datasets. Databricks' Spark jobs as well as Delta Lake Lakehouse backend is well equipped for these kinds of tasks.

Likelihood to Recommend

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

Use Cases and Deployment Scope

This product is used for Data Science project development, from data analysis/wrangling to feature creation, to training, to finetuning and to model test and validation, and finally to deployment. While Databricks is used by many users, we also use GitHub and code Q/A to promote a code in production. This is one of the advantages of Databricks is the integration part, not only Git but whether you use it on Azure or AWS, you can also leverage the power of the integrated Machine Learning in those platforms, such as auto ml or Azure ML.

Likelihood to Recommend

Currently the best Data Science tool for a large-scale company that needs strong tech support once and a while. The performance and the connectivity/integration with a large bread of tools and platform is also important when you don't want to change all your stack. DataBricks is a great non-drage and drops tool for real Data Scientist that knows their things.

Use Cases and Deployment Scope

It is currently used by our Data and Product teams in order to perform deep dives analysis on how our current metrics are performing (KPIs, OKRs), to develop tools for metric predictions based on data models in languages such as SQL and Python while mixing them and giving to the entire company visibility of the results with graphs via shared workspaces

Likelihood to Recommend

I reckon is an amazing platform for users with a certain level of expertise for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, also it is very useful when it comes to cross company shared workspaces for unified comprehension of the data.

it is less appropriate for users who don't have full knowledge of the tables they are going to query on and need more support on the data, since the platform doesn't give an option to see what are the fields in a table before even querying it

Use Cases and Deployment Scope

We currently use the Databricks Lakehouse Platform for a client. My team specifically uses it to data-mine, create reports and analytics for the client. Depending on where the data is stored, various Analytics teams in my company use different platforms - GCP, AWS, Databricks, etc.

Likelihood to Recommend

Databricks is great for beginner as well as advanced coders. The interface is extremely user-friendly and the learning curve is quite short. It is well suited for automation where we can have scripts running late at night when the load is less and wake up to an email notification of success or failure. It is also well suited for writing codes that require the use of multiple languages (in some cases of data modeling)

The ability to store temporary/permanent tables on data lakes is a fabulous feature as well. PySpark is an excellent language to learn and it works really fast with large datasets.

Use Cases and Deployment Scope

We build all our data pipelines with Databricks Lakehouse technology. It is reliable and the tech support from Databricks is very good.

Likelihood to Recommend

We can run data pipelines and use SQL Analytics to build dynamic dashboards for clients. The same platform can be used for running ML pipelines.

Use Cases and Deployment Scope

We use Databricks to replace traditional RDBMS like Oracle. We have Big Batch ETL, Ingestion and Extraction Job for Big data ran across different products where we leverage Lakehouse platform to put our raw data in Data Lake and Create Delta Lake platform based on high performing Parquet.

It is kind of proposed to use across the whole organization and different BU's. Databricks will be our key main virtualized platform.

It addresses very fast data ingestion, reduces the overall ETL window. Integrated different datasource and also helps to perform Machine Learning jobs to run and scale. Idea is to reduce overall computation time to save cost on onprem.

Likelihood to Recommend

Delta Share, Data virtualization , Open Data Integration with Other data sources, parquet ingestion

Use Cases and Deployment Scope

[Databricks Lakehouse Platform (Unified Analytics Platform) is] used by a few departments to start off with data warehousing. SQL analytics, real time monitoring and data governance.

Likelihood to Recommend

[Databricks Lakehouse Platform (Unified Analytics Platform)] makes the power of Spark accessible. Databricks's proactive and customer-centric service. It is a highly adaptable solution for data engineering, data science, and AI. Load times are not consistent and no ability to restrict data access to specific users or groups.