Amazon Redshift Information Reviews & Insights

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.

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.

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 have decided to purchase Amazon Redshift since we started the project of building a new "data lake," so the first step was to decide which tool would be more appropriate to use as a data warehouse. Since we have everything on the cloud, we choose Amazon Redshift to connect our current tools on AWS and integrate the data.

If you are looking for something easy to implement that will give you a nice performance, I would suggest Amazon Redshift. I'm using it in AWS environment, so I don't know if in another cloud environment the performance and all the features would be nice as well. It's also important to check if the price fits to you too.

AWS Redshift is the cloud-based data warehouse where we store our application level datasets and is used further for insights from the stored dataset. It improves the decision support for our business based on data analytics on a large set of real-time datasets which force the business processes to the next level. It provides good performance with high availability for essential data analysis and valuable intelligence.

Amazon Redshift is the data warehouse under the umbrella of AWS services, so if your application is functioning under the AWS, Redshift is the best solution for this. For large amounts of data, the application is the best fit for real-time insight from the data and added decision capability for growing businesses. If your application is outside of AWS it might add more time in data management.

Redshift is being used by engineering for our data warehouse or data lake, if you will. It's part of our ETL pipeline, where the data is used to form dashboards and analytical queries across all of our initially segregated data. So it is kind of a source of truth linking data across the company. These dashboards are accessible across other departments in the organization. The data is consumed by everyone, not just engineers.

Use Redshift for data warehouses, especially if your data is already in the cloud (AWS). It's great for large datasets, and fast too, even if your dataset is column heavy. It's less so for when you have a bunch of rows. All in all, it's a good starting point for any aspiring data warehouse, but there are other promising solutions too. E.g. Snowflake.

Amazon Redshift is being used as the main data warehouse across the whole organization. It provides essential business performance data and valuable intelligence. Different departments utilize it for a variety of operations, and decision makers rely on the data analytic tools built upon its data for tracking and enhancement, wherever needed.

It is best for large structured data sets with data normally loaded from files, instead of through heavy SQL data manipulation. If there are SQL data manipulation, better to have mainly DDL (create/drop/alter) instead of DML (insert/update/delete) to achieve the same goals. It is also better for smaller number of concurrent connections (less than 500).

Redshift is currently being used to house normalized client data pulled from various third-party endpoints. It houses the data that is both being accessed directly by our business intelligence and CRM platform, as well as made available via our own API gateways. It was chosen for its ability to support a "big data" environment with high availability.

It is well suited for:

• Petabytes of data requiring near real-time analysis
• Massive Data Insertions
• Massive Data Reads

It isn't well suited for:

• Web apps
• Smaller transactional inserts
• Smaller reads

You wouldn't drive an 18-wheeler to the corner store to pick up a bag of chips. Your specific need will determine whether or not Redshift is suited for the job.

We use Amazon Redshift as our data warehouse. We store our most used datasets on Redshift. Some of the data transformations are also made on Redshift but we try to avoid to do the heavy transformation there. Our dashboarding tool sits on top of Redshift and runs queries on every dashboard load. Analysts are using Redshift all day as the main source of data. Data savvy engineers and product managers have read access.

Redshift is a great data warehouse. It can serve the analyst team and most BI tools perfectly. Easy to learn and use. If you are using AWS then Amazon Redshift is a self-evident choice. You can use Redshift for ETL but It can be too expensive. It is not appropriate to serve as an in-production DB.

Amazon Redshift is being used by several of our clients for analysis of large datasets. In most cases, it is used at a department level, in conjunction with other on-prem and in the cloud data solutions, including data warehouses and relational databases.

Redshift is well suited as an alternative to on-prem data warehouses. AWS Data Migration Services can be used to migrate data from various relational databases into Redshift.