Amazon Redshift Reviews

Scalability: Ability to load huge number of datasets (I have experience with petabytes of data) and process those things. Storage is not limited. We can increase whatever we want.

Performance: The distributed architecture of Redshift has the capacity to process the workflow in a different cluster and coordinate those things in the leader node, making the process much faster.

Flexibility: This feature is helpful for user to increase the node size and config depending on their need. There is no need to wait for hardware to be in place whenever we increase the dataset. Redshift provides the option to increase the node or cluster size whenever required.

Multi-formatted accessibility: The Redshift engine has the capability to read the following file formats: CSV, DELIMITER, FIXEDWIDTH, AVRO, JSON, BZIP2, GZIP, LZOP. The user can choose which is best for their requirements.

VPC configuration: VPC configuration secures our dataset, which we keep inside the Redshift cluster. This VPC config doesn’t allow any third party in or out bound against firewall.

Python UDF calls: This is useful for a user to create their own user-defined function through Python and import that class into Redshift and process the dataset.

We were using MySQL & MongoDB for our regular operations, but when we grew, we were forced to handle a huge number of datasets. It could be petabytes of data in and out on a regular basis. We struggled a lot to complete the operations in a timely manner. With Amazon Redshift, we gained a lot in terms of timing, as well as project completion.

Some of the scoring mechanism really works well in the distributed architecture of Amazon Redshift.

Of course, every product has pluses and minuses. From that perspective, Amazon Redshift has some issues with snapshot restoring when we handle huge datasets. When our snapshot size is really huge, like 20 TB+, we are forced to wait a long time to get it restored. This is reasonable, as they need to transfer the entire dataset to the cluster.

My thought on this issue is that Amazon has their own data centers and they are connecting each region of storage through Direct Connect. The input and output network data transfer might not be a complex thing. For example, if they used 10 Gbps network transfer, they can transfer 1 TB in less than two minutes, but that’s not happening now. To restore 1 TB of data, it takes more than 30-40 minutes.

I have used it for the last 3.5 Years.

I am using Amazon Redshift for big data mapping and data aggregation.

We are using most of their products. Specifically, we are using their dedicated data-centre service (Direct Connect). We are using Amazon products such as Amazon EC2, S3, SQS, EMR, ML, CloudWatch, Redshift, DynamoDB, etc., for more than 10-12 years.

I have encountered stability issues. A few weeks ago, I encountered an issue with hardware failure and database health status failure. When we face these kind of issues, we can't do anything from our side until the Amazon technical team finds the issue and rectifies it. It takes time to get resolved. If we are in a rush to deliver something for a client and encountered these issue, we are really screwed.

Ofcourse. When the amount of data that we handle in the cluster grew, we need to increase the cluster or node size. Apparently, the size of node or cluster increases the hold time for synchronizing the data (meta data) with the node manager. The initial time increases when we start the cluster.

Customer Service good. But couldn't make direct call to customer service many times. I could catch them through their web UI rather making direct call.

Technical support is really great, but it’s paid support. The Basic Support plan doesn't have the option for technical support. It’s only providing billing support.

I have experience working in Hadoop as well. When I compare the two (Redshift & Hadoop), Redshift is more user friendly in terms of configuration and maintenance.

The initial setup of Amazon Redshift is so simple and straightforward. We do not need to read or understand any of the technical documentation. Simply said, it’s a plug-and-play service or platform.

I have implemented through in-house.

In terms of ROI, I can't directly convert to it. Because we are not using only Redshift. We are using multiple product to increase our revenue and decrease time consumption. So It's difficult to calculate ROI of Redshift usage.

Pricing and licensing is so important. In terms of pricing, it's bit high, as they are using standard hardware. My advice to users is: We need to start the cluster when we require it. At the end of the workday, we can just snapshot the clusters and shut them down. And then we restore those snapshots when we need them back. That way, we are charged only for usage rather than spending money on wait time or sleep.

I evaluated Hadoop and Spark, along with Redshift. I have no negative comments about those other products. Redshift is flexible in terms of configuration, maintenance and security, especially VPC configuration, which secures our data a lot.

Use this product for huge data mapping or aggregation. Use Redshift through VPC to keep their data very secure and for a long time.

We mainly use the solution for marketing analytics.

The solution works fast. I use Redshift to clear a lot of web page data. I use it mainly as an extraction tool to obtain the information I need for a project and store it in parquet files on a disk. Later, I work on the data using Python. I write back all my final results to Redshift and store the temporary files on a local machine.

They should provide a structured way to work with interim data than to store it in parquet files locally. Also, Redshift is unwieldy. There should be better integration between Python and Redshift. It could be more accessible without using so many sequels.

They should make writing and reading the data frames into and from Redshift easier. The performance could be better. I have used Redshift for extensive queries. For the large tables, it's easier to unload to Redshift, but subquery tables that run complex grids are slower for configuration. I have to use the unloaded command to unload the whole table. Further, I have to read the table into a server with extensive memory in Python and process the data ahead. It's not optimal. 

We have been using the solution for two years.

I rate the solution’s stability as a nine.

The solution is scalable. It handles different loads very well. We have 80-100 users using the solution in our company.

The setup was quite complicated. For instance, if you use AWS Glue to automate loads into Redshift, setting up the security for the requirements between the two is complex. I've struggled a lot to set up the cluster on VPC and to get all the endpoints set up correctly with the right access and services. Especially from Glue's endpoints, I had to repeat the same process every time. It consumes a lot of time. In comparison, the CloudOps executives do the setup very quickly.

I have heard complaints about the solution’s pricing, and thus I rate it as a five.

I rate the solution as an eight out of ten.

We use this solution for our enterprise data warehouse.

We use it for scaling our warehouse.

We also use it for analytics.

A feature I find most valuable in Amazon Redshift is that it's fast.

I also like that you can query the data lake using the Redshift spectrum.

You can build analytics on this solution, similar to QuickSight or Tableau, based on the Redshift Data API.

Amazon Redshift also has good integration and a good API. It's not hard to manage. It even has machine learning features.

This solution could be cheaper, but Amazon could constantly add new instances and decrease the size from previous ones, so it could be an AWS work model.

It would also be better if it can be integrated with non-AWS sources, e.g. additional open source connectors.

We've been using Amazon Redshift for a long time. We've been using it for years.

This solution is stable.

I find this solution scalable to a great extent.

Amazon's technical support works fine.

Setup was easy. This is a managed service, so you don't need to install anything. You just choose the size of the cluster and within 10 to 20 minutes, it's built.

This solution implements the pay-as-you-use model, so no license. It's just for underlying infrastructure.

I evaluated Cloudera and Azure.

We usually rely on some distribution, not like classic Hadoop. We do not use  open source Hadoop because our customers prefer some distribution to get support. Some paid features from Cloudera and Amazon provide their own support.

We provide expertise for different services of AWS, e.g. most of them because we are a premium partner and we have program competencies. We develop data, big data, machine learning, and migration, so we cover a large field.

We have some clients with Azure, but most of our clients are with AWS.

The deployment of Amazon Redshift is through AWS. It's a home-based, proprietary solution. It's fully managed by AWS.

Amazon Redshift has implemented most of the features that we currently need, so I can't name additional features that I expect from them in the next release.

The number of people needed for the deployment and maintenance of this solution in our organization is just one: only to set up. It's just one DevOps.

I would recommend Amazon Redshift as a solution for others who are thinking about using it.

I'm rating Amazon Redshift an eight out of ten.

We are premium partners with Amazon. 

This service can merge and integrate well with all databases. 

Planting is the primary key enforcement that should be improved but there is probably a reason that they don't follow the reference architecture. It means they are creating clones of the data shading. Cost control measures could be improved along with added transparency.

I've been using this solution for nine months. 

AWS technical support is very good. 

We needed some help from experienced professionals for our initial setup. 

Licensing costs are reasonable. 

I would recommend this solution depending on the scale. You need to decide whether you want to be lined up with a single cloud provider or go over the service and have it deployed on multi cloud. There are many factors to take into consideration. 

I rate this solution seven out of 10. 

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.

I like the cost-benefit ratio, meaning that it is as easy to use as it is powerful and well-performing. There are only three parameters that you need to understand, which are the distribution key, the sort key, and the compression method or encoding method. Once you understand these, you can tune the performance.

I would like a better way to ingest data in realtime because there is a bit too much latency.

There are too many limitations with respect to concurrency. It is now possible to auto-scale it, although that is still slow.

It could offer smaller nodes with decoupling of storage and processing because for the moment, the only nodes available to work that way are huge, and for large companies.

My first implementation of Redshift was three and a half years ago, in 2017.

We have not had many issues with stability.

Scalability can be a problem if you don't write your database queries correctly. For example, if you write a cartesian product in Redshift then you may end up consuming all of the resources. However, it does have features like workload management to prevent this from happening.

Our clients are mid-sized to very large companies.

I have been in touch with Amazon technical support and they are very good. They are efficient and resolve problems quickly. They know what they're doing and they're very professional.

I have also used Snowflake and its methods for ingesting real-time data are faster. It also offers a bit more functionality and a bit more flexibility. It's a bit easier to maintain and faster to scale, but more expensive as well.

To me, the big drawback with Snowflake is that the data is not stored in your AWS or Azure subscription, or AWS account. They store the data in their own account that they manage for you, which might be a problem for some companies in terms of compliance and legal requirements.

Azure Synapse and Google BigQuery are also competing solutions.

The deployment is very straightforward and it usually takes a couple of minutes. This is one of the reasons I like it.

As long as a person understands the AWS landscape, they can deploy it on their own. Otherwise, without realizing it, they might for example deploy a Redshift cluster that is not properly secure. Similarly, it could cost a lot of money if they don't know what they're doing. You don't need a very in-depth technical expertise, but you do need to understand how AWS works.

I have a team that provides maintenance for our customers. It is spread between France and Belgium and I have 25 people who report to me, with another 20 who I work with indirectly.

The cost of Redshift ranges from a few hundred dollars a month to thousands of dollars a month, according to the resources that you're going to use, the number of nodes, and the type of nodes.

My customers have implementations that cost about $500 a month for a very small one. I also have a customer with a monthly invoice of about $25,000 USD.

With the most recent update, we should now be able to decouple storage from processes.

My advice for anybody who is implementing Redshift is to make sure that they are using it for what it is made to do. It's an analytical database, so it's not meant to process transactional data. It's the perfect tool if you use it for the right purpose.

Overall, it is a very stable and robust product. That said, there is still plenty of potential for improvement.

I would rate this solution a seven out of ten.

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.

I've been working with data warehouses for a long time and it has always been the case that we had to invest quite a bit on infrastructure, upfront. We are used to dealing with Teradata, and the cost of setting up the data center and getting the appropriate licenses was a big deal. Now, we are able to spin up some clusters and then start using it, allowing us to incrementally pay as we expand.

This has become a big shift in how we spend because there is no capital cost upfront. Moreover, this works with startups as well as with enterprise, and they provide an equal footing. This means that even the advanced capabilities and insights that are available with a data warehouse are no longer limited to the larger clients. Even a startup can use these features, immediately.

The most valuable feature is the scalability, as it grows according to our needs.

The part that I like best is that you only pay for what you are using.

The OLAP slide and dice features need to be improved. For example, if a business wants to bring in a general ledger from an ERP, they want to slice and dice the data. What we have found is that they have a lot of formulas that are used to calculate metrics, so what we do is use SQL Server Analysis Services. The question then becomes one of adopting a single vendor and transitioning to Azure. If Redshift had similar capabilities then it would be very good.

We have been using Amazon Redshift for about five years.

The stability is awesome. We have been using it for quite a while and haven't faced any issues.

The scalability is very good. You can start at a very low scale and just keep expanding as required. It is the type of product that fits organizations of all sizes.

We have contacted support on several occasions. With our most recent customer, they are pretty large and we were directly in touch with the regional account manager, who is the head of database analytics for India. This person was directly involved in our calls and helped with the evaluation, so the support has been pretty good.

We have worked with Teradata and more recently, have been working with Azure SQL Warehouse. Teradata is an on-premises solution and the upfront costs are high. Comparing Azure SQL Warehouse and Amazon Redshift, in terms of features I think that they are pretty much on par.

The SQL Data Warehouse does have better OLAP capabilities, and they also offer a level of serverless capability where they have split the compute and the storage. This means that they can operate at a lower cost in the development environment.

Many of our clients have begun to adopt Power BI, and once they start using it, they tend to lean towards Azure and the Azure SQL Data Warehouse. The fact that Power BI is free, makes quite an impact.

The initial setup is straightforward, once you get used to it. There is a lot of documentation available.

We handle the implementation and deployment of Redshift for our clients.

I am interested in seeing a split between compute and storage, which is something that they are currently working on. We plan to start leveraging it at some point in the future.

In summary, I think that Amazon Redshift is a very good data warehouse and we really like it a lot.

I would rate this solution an eight out of ten.

Redshift COPY command, because much of my work involved helping customers migrate large amounts of data into Redshift.

It helped my customers migrate off on-premise platforms such as Teradata to Redshift, at a fraction of the cost.

There are challenges with dealing with character set mismatches. Migrating data from other data sources can be challenging when you are working with multibyte character sets.

Two years.

No.

I personally haven’t hit scalability issues but at dinner a year ago with a few of my existing customers (all Fortune 500 companies), I was told there are scalability issues once you get to 32-nodes.

One of my previous customers told me they were migrating off Redshift because they hit the ceiling and had scalability issues. They told me the responsiveness they were getting was inferior to alternative solutions once your Redshift gets to a specific size.

I never utilized AWS technical support.

I’ve helped customers migrate off Teradata, SQL Server , Oracle Exadata, Greenplum, and ParAccel Matrix to Redshift. Some due to cost savings, others because of the EOL of the product.

Setup of Redshift infrastructure is pretty straightforward. I’ve been told that setting up partitions can be tricky in order to ensure good performance.

I have nothing to add here as I wasn’t involved in this part of the process. However, one of my customers went with Google Big Query over Redshift because it was significantly cheaper for their project.

I only provided advice to my customers, but some looked at Azure SQL DW , Greenplum, Netezza, and Google Big Query as possible alternatives

Be careful with vendor lock-in! You cannot move your Redshift environment to a different cloud provider or to an on-premise solution.

• It is based on PostgreSQL.
• It’s managed. Meaning, AWS takes care of handling infrastructure, deployments, encryption, and uptime for you.
• It’s cheap when you consider the price per terrabyte per year.
• It’s integrated into the AWS stack.

At my previous company that does mobile analytics as its core product, we moved all the analytics backend from MongoDB to Redshift. Where I currently work, we use it as our main data lake/data warehouse.

While It's probably the best product of its category (managed SQL-based data warehouse at scale), it has a few shortcomings, although very few.

The main issue people complain about, and I agree with the claim, is that it's hard to load your data into it. You need to first export your data on S3 as CSV, JSON or AVRO. Then you can load it into Redshift. And even then, you have to make sure your data is properly formatted. (you can use the copy options: TRUNCATECOLUMNS to load fields that are too big, and MAXERROR to allow for a given number of errors while loading). In general, ETL and data cleaning is a hurdle in data engineering, and Redshift suffers from it.

I have used Redshift for three years.

I once had an issue because my data contained a Unicode NULL character in a VARCHAR field ("\u0000"). The AWS support has been very quick and helpful to respond. Other than that, I have had no issues whatsoever.

No scalability issues whatsoever.

Technical support is very good.

At my previous company, we switched from MongoDB to Redshift. The main reason was price and performance. At my current company, we started a data warehouse (greenfield project). The choice was between Google BigQuery and AWS Redshift. The main criteria was that Redshift was PostgreSQL-based and supports CTE and Window functions (PostgreSQL features).

The big part when using Redshift is setting up the ETLs and doing the data cleaning. It was very hard when moving from MongoDB, because I had to re-discover our data schema (that had no spec). With that said, in both cases (moving from MongoDB and starting from scratch), I had a prototype up in about a day. By that I mean that I had the most important parts of my data loaded into Redshift and I could query it.

The pricing page is explicit. Choose what suits your needs in terms of storage and performance.

For setting up a data warehouse, BigQuery was a serious contender. BigQuery is simpler to setup and scale. It's also more of a black box: you worry less what's inside and how it scales and you get charged for what you consume (which is both a pro and a con). With Redshift, you choose in advance the type of machine you want, like EC2 (resizing your cluster is easy).

If you evaluate Redshift, chances are that you should evaluate BigQuery too. So take the time to weigh the pro and cons of each (plenty has been written online about that).

Take a look at the reserved instances pricing. It is very advantageous if you know you will stick with Redshift for some time.

Take the time to learn PostgreSQL (eg: https://www.pgexercises.com/). Redshift, while based on PostgreSQL 8.0, supports a good number of advanced Postgres features.

Do not be afraid of joins. PostgreSQL is performs very well in this regard.
If you need performance, have a look at the suggested optimizations in the official documentation (such as setting up the correct distkeys, sortkeys and compression schemes).

Understand that Redshift has no indexes.

Understand that Redshift is an analytical database with columnar storage, and that it does not enforce constraints.

Redshift plays very well with a PostgreSQL instance in RDS linked to it via DBLINK (see this guide: https://aws.amazon.com/blogs/big-data/join-amazon-redshift-and-amazon-rds-postgresql-with-dblink/). I've used this in production at my current company, and this is tremendously useful. You can have your raw data in Redshift and aggregate it directly into RDS. To do this, insert into RDS what you select from Redshift through the dblink.