Amazon Redshift Reviews & Insights

We are using Amazon Redshift as a warehousing solution, where we are doing multiple ETL sync from clickstream events as well as transaction DBs.
We are doing analytics on the top this data and utilise this data to build and train data-science models.
We are in gaming industry we are solving business problem such as increasing the number of user gameplay, increasing the revenue, increasing the registration as well as the acquisition.

For data integration using Amazon MSK and seemless integration with Transaction DB.
Faster data retrieval with complex joins as well as it is giving functionality to add dist key as well as sort key to make the performance better.
Vacuum and Analyse command for improvement is the cheery on the top.

We are using Amazon Redshift as our main data warehouse to store most of our data, the whole process consists in extract the data from different sources, we do some transformations when needed and the data is finally stored in Amazon Redshift in order to be used afterward by one of our Business Intelligence tools.

If you are looking for a data warehouse where you don't need to worry about maintenance and scalability, Amazon Redshift should be one of your options once it is a self-managed data warehouse with many connectors and easy usage as well. Besides that, if your environment runs on AWS, it is even easier to integrate.

We use Amazon Redshift for structured data warehousing. It allows us to store, retrieve, and analyze large volumes of structured data quickly and efficiently. It is used to support decision-making, identify trends, and gain insights into the business. Furthermore, we use Amazon Redshift can be used to create dashboards, generate reports, and perform ad-hoc queries on data to support business intelligence and analytics efforts. We also use it to support our customer service applications or fraud detection systems

Amazon Redshift is well-suited for a variety of scenarios where businesses need to store, retrieve, and analyze large volumes of structured data. Some specific scenarios where Amazon Redshift may be well-suited include: Data Warehousing, Business Intelligence, Data Migration as well as Real-Time Data Processing. On the other hand, Amazon Redshift may not be the most appropriate for unstructured data, organizations with low volume of data or Real-Time Stream Processing.

I use it as the data warehouse of our clients. I use it to build data transformations of user activity logs to ML features. I use the sql workbench to explore datasets and understand data schemas. Post that, I generally connect to the warehouse either through dbt or from jupyter notebooks.

It is a solid data warehouse on top of the AWS ecosystem. If most of your infra is on AWS, it makes good sense to go for it. But it is expected to be tuned well by a data engineer for an optimal performance. For a data scientist too, the SQL is a bit limited when it comes to unstructured columns in the tables. Arrays, jsons, etc have very poor support compared to other warehouses.

Amazon Redshift is primarily used as a data management solution by Product Analytics Group. We currently have various sources of capturing data like Heap, Delighted, Salesforce and it is convenient to build an ETL from these sources to Redshift. This enables us to merge all these data sources into single view in a BI tool like Power BI

It is well suited in scenarios where you have distributed data sources and would like to build an ETL pipeline with limited data engineering efforts. Operations time and cost is relatively low compared to other tools. Also it offers great connectivity with Heap with no technical know-how required. It is mostly self managed and reliable.

Amazon Redshift is our Data Warehouse, where we store our processed data (Hot data) for various initiatives like BI, Analytics, DataScience, etc

We also use Amazon Redshift Spectrum as our Data Lake, where we store raw (un-processed) data (Cold data) for historical analysis, trends, etc

We store various standard data in Redshift like:
Bronze (ETL-ed data),
Silver (Materialized Views data), and
Gold (Rollups/Aggregated/Dashboard-ready data) in [Amazon] Redshift

[Amazon] Redshift is suited for various use cases like Time series data, Structured / relational data, Semi structured data like JSON, etc.

[Amazon] Redshift might not work 100% well with full performance, for Graph DB use cases.

I am working with an insurance client where a lot of claims and policy data comes in every day, we use amazon redshift to perform ETL and analyze the data to gain business insights.
Amazon redshift is an integral part of our data analysis.

Amazon redshift is best suited for data analysis and is not suited for transactions.
for eg. you can use amazon redshift to gain insights from a large data set but cannot use it to do a transaction level update and insert

It is used within a few departments. It is used to solve certain legacy problems that have not yet been ported over to other more suitable approaches.

It's appropriate for ad hoc queries on semiorganized data.

We use Amazon Redshift for our insights platform in our R&D space. Our team creates reports and dashboards on tools for business use. Amazon Redshift provides greater supply chain visibility, increased information on product movement, and high efficiency at a much faster rate.

Amazon Redshift performs extremely well for reporting/analytics data and is way ahead of other competitors. The biggest challenge is migrating data from on-premises databases to Amazon Redshift. The initial hurdle is a major one.

Cloudwalker offers analytic services for the gambling industry. The gambling industry has vast amounts of data that are high speed and variability. Our services from Redshift help gambling companies have better control of their bookmaking product, have a complete view of customers betting history, helps with detecting problematic accounts, etc.

Redshift is great data warehouse solution if you have several billion rows tables. More than 200 very important improvements were added in several years' time. With new Redshift instance types solution has separation of storage and compute and magnitude better query response times. Don't use Redshift if you have less than several billion rows tables.