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.

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.

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. 

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 am providing resources to our developers and application team. We use Amazon Redshift from a machine learning perspective. We are currently evaluating some tools for machine learning.

I am using Amazon Web Services from a machine learning perspective. They provide some tools, which we are currently evaluating for machine learning. With the tools we have, Amazon Redshift has the features to query efficiently. Security configurations are implemented across all processes, such as AWS Config and GuardDuty.

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.

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.

Amazon Redshift's main advantage is that in our company, we can easily document it whenever we put in some data, meaning we need to take care of the documentation. The tool can also be used for data errors, so we can see the query number of records, and it is also very easy to use for queries.

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 product is relatively easy to use because there is no indexing and no partitions. There is no referential integrity only declarative, which is okay.