Amazon Redshift Reviews

We use Amazon Redshift for our data lake. It serves as our organization's data mart.

Amazon Redshift is a combination of features. It is basically a cluster that works better with Glue when it comes to incremental loads. It can handle a serverless architecture, and at the end of the day, it is a database. Amazon Redshift's performance optimization and scalability are quite helpful, providing functionalities such as scaling up and down.

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.

The tool's most valuable feature is its parallel processing capability. It can handle massive amounts of data, even when pushing hundreds of terabytes, and its scaling capabilities are good.

The solution's performance and speed depend on the data structure and model, but Amazon Redshift's massive parallel processing is typically good. 

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.

The most valuable feature is its seamless integration within the AWS ecosystem. If your data is already in S3, it becomes easier to port it into Redshift and work with it. 

Redshift allows you to transform different data formats and consolidate them into one Redshift cluster. This means you can transform various siloed data sources like Excel files and CSV files into Redshift. 

With Redshift, you get a suite of applications that enable you to drop a file and consume it into the data warehouse, facilitating the use of Power BI dashboards, Tableau dashboards, or custom dashboards on top of Redshift data. This setup process is much smoother compared to other tools that may require going through legal MSA (Master Services Agreement) and other complex procedures.

We primarily use Amazon Redshift for visualization or connecting to our BI tools, such as Tableau and Power BI. Nothing beyond that.

As a developer and for our users, we have had a good experience connecting Tableau or Power BI to AWS Redshift and building visualizations and BI analytics reports. Migrating from on-premises to cloud, we have found good performance on Redshift as well.

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.

The tool is the one place where the company’s data is stored. We use it extensively. Previously, we had data in multiple databases and structures. Since we started using Redshift, everything is in one place. We have multiple supporting layers and dashboards connected directly to Redshift. It helps and supports all the different businesses.

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.

Service accounts are used in both Amazon Redshift and Google Cloud. For example, I could create a service account for my desktop to access Redshift or a service account for multiple users to access Redshift. In BigQuery, creating a service account is very simple, and you get full control over the access, so you can limit what the service account can do. This prevents accidental exposure of data or deletion of data. Only certain features are available, which is very handy.

Postgres syntax requires 25 synthetic scrubs to Postgresify. It's handy, but there are no blockers when using the query. It's more competitive, but the price is very reasonable. I was always aware of what I would pay, and if I reserved servers, I knew what it would cost. There is no alternative in choosing a solution. We had to use the server version of AWS, but it had limited features. A few features were lacking, which couldn't front Redshift against it or access it from the API. We had our nodes, which were sent from Amazon. It has a minimal setup, with two services running only. 

It was predictable because the performance was good. When a complex BBT model was running, we reached its limits. If there was a one-node setup, not all the storage was available on the server. For example, in a machine with 72 gigabytes of storage, only four were available in a single setup. I had another node, with 64Gb. All the storage of the two servers was available and when you are running these complex queries, it's not only a bit of computing but also temporarily eats up the storage. I couldn't use a single server because temporary tables ate up the storage. BigQuery’s authentication is straightforward. Besides that, it's doing what it's expected to do. There are no major problems.

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.

For reporting purposes, Redshift is a great tool to use. Redshift has an advantage when it comes to administration, making it easier to manage and collaborate. Additionally, its server architecture allows for faster processing. Redshift also supports prepaid costs, which is another great feature. However, similar features are also available in Azure.

Redshift has some advantages in terms of administration and performance.

I used it as part of the Amazon Connect integration; I had to implement Redshift for a couple of customers. It's used for various use cases involving reporting and exporting data to external sources. I have also used it for some analytics integrations.

The use cases I have typically worked on involve transferring Amazon Connect data to different systems for analytics. The two or three deployments I have done with Redshift are more or less similar because it acts as a kind of data middleware. 

Redshift effectively gathers data from various sources and facilitates the integration of that data into different destinations. This is typically used for insights collection, data showcasing, and integration into a standard ETL process.

So, the overall performance and speed of Redshift have affected the query times.

For the use cases I worked on, particularly on the Connect side, the query times with Redshift are pretty straightforward. We started using Redshift for these cases, and it significantly helped. To achieve faster results from Redshift, we first need to optimize the queries. It does reduce a lot of time in how data is gathered and then presented from the queries.