What is your primary use case for Amazon Redshift?

I mostly use Amazon Redshift for data warehouse purposes. I have used it as the BI tool source and for making data transformations and keeping them stored permanently. These have been one of the primary use cases most of the time.

We use Amazon Redshift in our business intelligence ecosystem. It's simple to configure, cost-effective, and close to our data sources.

I use the solution in our company's project. Currently, our company uses the tool to connect two parts. Firstly, my company uses Amazon Redshift by connecting it with AWS Glue to create S3 files. My company is involved in the creation of external tables as we are shifting the data into Amazon Redshift. Secondly, my company uses the database, and we have written the code by creating the external tables while storing the data on Amazon S3.

I use the solution in my company for our data warehouse and databases.

We use the solution to build a data warehouse schema for a target database for analytics. We are uploading data from different transactional databases into Amazon Redshift. We use it for reporting purposes. We use the tool mainly for querying and retrieving the data for analytics.

The solution can be used as a warehouse. We dump any data that exists in our company into it. We spring up different databases based on the requirements.

Amazon Redshift serves as our data warehouse system. We collect data from various sources, including 153 streams. We gather companies' data for rating, deployment, and stock market analysis. We then push this data onto Amazon Redshift, which Power BI, Tableau, and even Google Looker use for reporting and analysis.

We use the solution for data storage of reports.

Redshift is an AWS warehouse solution. We have structured datasets, and we don't load all the amplitude data into Redshift. We first do this via Hudl, a data integration solution partner, but then later, it's directly loaded by an interaction. Then we run DBT against Redshift. We have our data models in DBT, and we run data analytics threats against the data warehouse.

There are many use cases, as I've worked with Amazon Redshift at different companies. Initially, we used it as part of the AWS suite, which made it easy to get started. We were able to move data from MySQL and PostgreSQL into Amazon Redshift, and we even used it in a production environment. However, the scalability of Amazon Redshift was not enough for our needs, so we switched to Snowflake.

I have used it for our reporting solution requirement. We gathered data from different processes and applications, like the high system process. Clients can review the data; we use it for connections and reports. Additionally, Redshift generates some configuration files without using an application.

We use Amazon Redshift to store customer data. Basically, it's just to store our customer data so that we can use our own data.

We use it for data warehousing. Currently, I'm setting up a data link with Redshift to fetch data from our data lake.

We use Redshift Spectrum for creating temp tables during the Ignition process.

We mainly use the solution for marketing analytics.

We were using the solution for our data backup, but we wanted to optimize it, so we turned to AWS Glue. Amazon Redshift wasn't really great for us and wasn't working out.

We use Redshift as a central data warehouse. This data can be consumed directly by AWS services or different applications where we can provision the data via Athena. If we want to create more business intelligence tools and analysis, we write some SageMaker notebooks for deploying machine learning models based on the data. We have a wide variety of use cases, but we generally use it to have one place where we can store data. We also connect enterprise legacy systems such as SAP systems. I'm mostly connecting shop floor assets and the industrial machinery to store relevant data in one place and make it available. Via Athena, combined data can then be retrieved with easy SQL queries. Usually, we use the newest version of the solution. If there are new updates available, we try to take them directly. Our customers usually have a hybrid cloud or full cloud architecture with minimum on-premises data centers. We have five to ten customer projects per year in our department in which we set up data lake houses using Redshift. Those companies have between 3,000 and 15,000 people. Not everybody has to use Redshift. Depending on the project and the size of the consulted company, there are 2,000 to 3,000 end users who need access to the stored data.

I'm a freelancer/consultant, so I use the solution across a wide variety of contexts.

It is storing warehouse data for the organization. We commission data warehousing, storage of data, and reporting.

If you want to create an enterprise data hub, that is where Redshift is used. Snowflake, Redshift, BigQuery, and Azure Synapse are enterprise data warehousing and cloud data technologies. Large enterprises have enterprise data. They have a lot of managed processes, business processes, customers, products, different assets, locations, equipment, etc. Then they have sales and marketing. There's a huge amount of data that is generated, and they will need a large warehouse or multiple data warehouses to create analytics out of that data. We try to tell organizations to consolidate all their data into a single unified data platform that has all the enterprise data rather than being processed by multiple warehouses. It's processed on one central data platform, which is cloud-based. In which case, we recommend one of these four. We either recommend Snowflake, Azure Synapse, AWS Redshift, or Google BigQuery. It depends on what their early investment is and what kind of work they need to do. Redshift is completely Managed on AWS cloud.

We use Microsoft Azure, but one of our clients is on AWS, so we did a POC for Amazon Redshift. Now, it's up to them to make the call on whether to continue with the solution based on pricing and their needs.

We primarily use Amazon Redshift for prediction modeling and business reporting.

We are using the latest version of Amazon Redshift, although I cannot state which one. It is actually updated automatically, which prevents us from tracking it. We use Amazon Redshift as our data warehouse solution. We use it for collecting and analyzing our data. We store our data for analytics in the solution.

We are premium partners with Amazon.

Redshift is a managed service for data warehouses.

Our primary use case of this product is as a data warehouse. We are partners with Amazon.

We are a solution provider and Amazon Redshift is one of the products that we implement for our clients. We are working with a few customers that have it implemented right now.

We are a service provider and we currently have five clients with active IT implementations that use Amazon Redshift. We also use it ourselves. My clients primarily use this product for data analytics. They are mostly working with big data and using the machine learning functionality.

We are a digital transformation services company, and we are using Amazon Redshift for one of our clients. They are a logistics company that has transportation and other needs. Their first requirement is for financial reporting, where we pull financial data from their many ERP systems and can provide a corporate-level view. There is also an operations standpoint, where they are looking for operational insights. For this, we again pull different information from their ERPs, bring it into Redshift, and then model it in such a way that they will be able to see a consolidated view in terms of operational success across lines of business.

We stored all of the data in the S3 bucket and would like to have it stored in a data warehouse, which is why we chose this database. It would be very easy for us as an end-user, who would like to access the data, rather than draw it post-transformation and store it at a database level.

We use Amazon S3 along with RedShift for storing our data. The data comes from various sources, including our client and third-parties. We get the data as an S3 file and then load it into RedShift using the ETL tools. RedShift will then act as the data source for Tableau, which is used for forecasting and other marketing activities.

My primary use for Amazon Redshift is for analytical purposes.

I'm the head of Data Warehouse and Business Intelligence, and our company is an Amazon customer. Most of the company's data sources were on Amazon at the time the product was deployed so it was logical to use this database. The data warehouse is quite small, compressed it's maybe 160 GB. It's not like an autonomous data warehouse or Exadata, which was almost 40 terabytes. It's a simple method of achieving extraction and loading. There is no real incremental load on this. Of course in the future, with the company growing, this should be changed and we'll probably need some kind of incremental system instead of this approach.

The primary use case for this solution is related to statistical data.

We primarily use the solution for analytics.

We are using the private cloud model of this solution. Our primary use case is for a data warehouse for BI.

We use it to build a data warehouse and a centralized location for all of our data sources, allowing for in-depth analysis by using SQL queries.