Our company uses the solution for big data and as an interface for analytics. 

We also create custom APIs to get data and provide SQL endpoints so users can access it over traditional tools like JDVC or ODBC. 

We use the solution on AWS and Azure. The data lake is wide open for departmental use. We have ten departments and two or three people from each department access the solution. 

Our company uses the solution for series-based and panel-based migrations. We collect and store user requirements, use apps to fetch data, and provide customers with better data for business reports. There are 30 to 40 users in our company.

Our company uses the solution's Spark module for big data analytics as a  processing engine.  

We do not use the module as a streaming engine. The historic perception is that Spark is for batches, machine learning, analytics, and big data processing but not for streaming and that is exactly how we use it. 

We use this solution for finding anomalies and applying the rules to the streaming data.

There are around 50 people using this solution in my organization, including data scientists.

I am a Databricks service partner, and my customers use Azure Databricks and Data Factory.

My company uses Databricks to process real-time and batch data with its streaming analytics part. We use Databricks' Unified Data Analytics Platform, for which we have Azure as a solution to bring the unified architecture on top of that to handle the streaming load for our platform.

I worked with Databricks pretty recently. The particular design processes involved in Databricks were also a part of that specific design/architectural process.

We have used the solution for the overall data foundation ecosystem for processing and storage on a Delta format. We have also seen use cases where we were trying to establish advanced analytics models and data sharing where we leverage the Delta Sharing capabilities from Databricks.

We're using it to provide a unified development experience for all our data experts, including all data engineers, data scientists, and IT engineers. 

We use the solution for data engineering. 

We use this solution to build skill and text classification models.

Our primary use case is in our project; we are dealing with Duo Special Data, where we need a lot of computing resources. Here, the traditional warehouse cannot handle the amount of data we are using, and this is where Databricks comes into the picture. 

Our primary use case for this solution is research for data scientists. The solution is deployed on cloud.

Our primary use case is ETL.

It's mainly used for data science, data analytics, visualization, and industrial analytics.

We mainly use Databricks to process ingest and do the ELT processes of data to get it ready for analytics and to serve the data to ThoughtSpot, which calls queries and Databricks to get the data.

Our primary use case is for data analytics. Essentially, we use it for the financial reporting for Adobe.

We use this solution for advanced civilization power.

We work with clients in the insurance space mostly. Insurance companies need to process claims. Their claim systems run under Databricks, where we do multiple transformations of the data. 

I've worked with Databricks primarily in the pharmaceuticals and life sciences space, which means a lot of work on patient-level data and the predictive analytics around that.

Another use case for Databricks is in the manufacturing industry. I'm a consultant, so the use cases for the product vary, but my primary use case for it is in the pharma space.

We use the solution for data analytics of industrial data.

We use it for data analysis and testing of high volume web user behavioral data.

I use Databricks for customer marketing analytics.

The product has helped in data fabrication. 

We use this solution for the Customer Data Platform(CDP). My company works in the MarTech space and usually we implement custom CDP.

We use the solution for reliability engineering, where we apply ML and Deep Learning models to identify the fear failure patterns across different geographies and products.

We use Databricks to define tool data and have many use cases to analyze and distribute the data.

We primarily use Databricks for reporting and machine learning.

We use Databricks for data science work in projects that create data pipelines, pre-processing, data wrangling, big data cluster management and ML, machine learning and deep learning tasks.

We were using Databricks to build an AI solution. We are only evaluating it, we have approximately three people that tried it out. Later we choose another solution, we did not fully deploy Databricks.

Our primary use case for the solution is data analysis by providing a Spark cluster environment with a driver to analyze a huge amount of data and gigabytes of data and can create Notebooks in Databricks. We can write SQL commands, Python code, Scala, or Spark with Python. With Databricks, we get a cluster hosted in the public cloud and we adjust it based on how much we use it.

We build data solutions for the banking industry. Previously, we worked with AWS, but now we are on Azure. My role is to assess the current legacy applications and provide cloud alternatives based on the customers' requirements and expectations.

Databricks is a unified platform that provides features like streaming and batch processing. All the data scientists, analysts, and engineers can collaborate on a single platform. It has all the features, you need, so you don't need to go for any other tool. 

We primarily use the solution for retail and manufacturing companies. It allows us to build data lakes.

We are using Databricks to receive the data from Data Lake where we are processing it and doing the transformation, and cleansing. Once it is processed, we are sending the data to the Azure SQL database.

I use Databricks to explore new features and provide the industry visibility and scalability of Databricks to the companies that I work with.

I create proof of concepts for companies. As a consultant, I also create training courses on Databricks. If a company wants to leverage a service provided by Databricks and needs to train people, they use our courses.

We are using Databricks for machine learning workloads specifically.

Databricks aligns well with our skillset and overall approach. We sought out their solution specifically for a big data application we are currently working on, as we needed a platform capable of handling large amounts of data and building models. Additionally, the fact that they use open-source software and can integrate data warehouse and data lake systems was particularly appealing, as we have encountered such issues in the past. We determined that Databricks would be an effective solution for our needs.

We use Databricks for batch data processing and stream data processing.

Our use case is confidential, but I can say we use it for a deep learning model for machine learning. 

Our team is currently utilizing machine learning for various applications, and a few members are also exploring Databrick's use for ML operations.

We use Databricks for video streaming and security purposes.

I believe we are using the new version.

Our company makes comprehensive use of the solution to consolidate data and do a certain amount of reporting and analytics. All the data consumers use Databricks to develop the information.

We have a team that works on Databricks for our clients. We are customers of Databricks. 

We are using this solution to run large analytics queries and prepare datasets for SparkML and ML using PySpark.

We ran on multiple clusters set up for a minimum of three and a maximum of nine nodes having 16GB RAM each.

For one ad hoc requirement, a 32-node cluster was required.

Databricks clusters were set for autoscaling and to time out after forty minutes of inactivity. Multiple users attached their notebooks to a cluster. When some workloads required different libraries, a dedicated cluster was spun up for that user.

Databricks is very useful and can handle thousands of different use cases. The use cases are all over the place.

Our primary use case of this product is for our customers who are running large systems and looking for an API -- a quick, easy integration with their own system. We use Databricks to create a secure API interface. I'm vice president of data science and we are customers of Databricks. 

Our primary use case for this solution is for data ingestion and the DQ rules we are implementing. We deploy the solution on Azure cloud.

We use this solution to process data, for example, data validation.

We use the solution for business engineering.

I am using Databricks in my company.

The primary use is for data management and managing workloads of data pipelines.

Databricks can also be used for data visualization, as well as to implement machine learning models. Machine learning development can be done using R, Python, and Spark programming.

We used Databricks in AWS on top of s3 buckets as data lake. The primary use case was providing consistent, ACID compliant data sets with full history and time series, that could be used for analytics.

My division works with Big Data and Data Science, and Databricks is one of the tools for Big Data that we work with. We are partners with Microsoft and we began working with this solution for one specific project in the financial industry.

We are working with Databricks and SMLS in the financial sector for big data and analytics. There are a number of business cases for analysis related to debt there. Several clients are working with it, analyzing data collected over a period of time and planning the next steps in multiple business divisions.

My organization is a professional consulting service. We provide services for the other organizations, which implement and use them in a production environment. We manage, implement, and upgrade those services, but we don't use them.

Our primary use case is to decrease costs and prevent any security press on data. I'm an IT manager and we are customers of Databricks. 

Our primary use case is really DevOps, for integration and continuous development. We've combined our database with some components from Azure to deploy elements in Sandbox for our data scientists and for our data engineers. 

Currently, I am using this solution for a forecasting project.

I am a data scientist here and that is my official role. I own the company. Our team is quite small at this point. We have around five people on the team and we are working with about five different businesses. The projects we get from them are massive undertakings. Each of us on the team takes multiple roles in our company and we use multiple tools to help best serve our clients. We are trying to look at creative ways that different solutions can be integrated and we try to understand what products we can use to create solutions for client companies that will be effective in meeting their needs.  

We are personally using Databricks for certain projects where we want to consider creating intelligent solutions. I have been working on Databricks as part of my role in this company, trying to see if there are any kind of standard products that we can use with it to create solutions. We know that Databricks integrates with Airflow, so that is something that we are exploring right now as a potential solution for enabling a creative response. We are exploring the cloud as an option. Databricks is available in Azure and we are currently figuring out the viability of using that as a cloud platform. So we are exploring the way Databricks and Azure integrate at the same time to give us this type of flexibility.  

What we use it for right now is more like asset management. If we have a lot of assets and we get a lot of real-time data, we certainly want to do some processing on some of this data, but you do not want to have to work on all of it in real-time. That is why we use Databricks. We push the data from Azure through Databricks and work on the data algorithm in Databricks and execute it from Azure with probably an RPA (Robotic Process Automation) or something of that sort. It intelligently offloads real-time processing.  

I primarily use the solution in two conditions: machine learning and big data computing.

I am using Databricks for creating business intelligence solutions.

I use this for database machine learning, to construct different models for supermarkets, drug store management, and market involvement to identify business opportunities for clients.

We provide different statistical models and use different algorithms depending on the client.

I was a Lead Data Scientist in different companies. I implement data and build and optimize processes using machine learning techniques, aided by science and advanced analytics.

My company is a customer of Databricks. We use Data Science products for machine learning, engineering, and data preparation.

We have between five and eight people working on coding in Databricks. Indirectly, we have 1500 people consuming the data. We have plans to increase the usage of data bricks by 30% next year.

Our primary use case of Databricks is for advanced analytics. I'm the chief research officer of the company and we're customers of Databricks.  

We use this solution for streaming analytics. We use machine learning functions that output to the API and work directly with the database.

Databricks can be used for large-scale data pre-processing and data transformations.

I am a developer and I do a lot of consulting using Databricks.

We have been primarily using this solution for ETL purposes. We also do some migration of on-premises data to the cloud.

We specialize in project consulting for our clients. Whenever we get the opportunity, we recommend Databricks to them.

Our primary use case for the solution is to run batch jobs.

I primarily use Databricks for data pipelines.

We are a consulting house and we employ solutions based on our customers' needs. We don't generally use products internally.

I am a certified data engineer from Microsoft and have worked on the Azure platform, which is why I have experience with Databricks. Now that Microsoft has launched Synapse, I think that there will be more use cases.

We use the solution for multiple items. We use lots of data crunching, development, and algorithms on it.

We are building internal tools and custom models for predictive analysis. We are currently building a platform where we can integrate multiple data sources, such as data that is coming from Azure, AWS, or any SQL database. We integrate the data and run our models on top of that.

We primarily use Databricks for data processing and for SQL databases.

We primarily use the solution to run current jobs; to run the spark jobs as the current job.

We use Databricks for experimentation. For example, we do ML model building and training that is connecting to our data which resides in Azure. It offers very good integration with Azure. We've deployed some of our model inference tools in Databricks.

We are still exploring the solution. We utilize it much, much better than their star schema models that they are trying to replace it with. We bring in Databricks and then see how they can leverage the additional analytical functionalities around the Databricks cloud. It's more in exploratory ways. We recommend Databricks, especially with the Azure cloud frameworks.

Databricks is the full data analytics platform. It involves end to end data analytics process.