Amazon Redshift Reviews

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 using the private cloud model of this solution. Our primary use case is for a data warehouse for BI.

The most valuable features are that it's easy to set up and easy to connect the many tools that connect to it.

Compatibility with other products, for example, Microsoft and Google, is a bit difficult because each one of them wants to be isolated with their solutions. That's a big problem now.

It is stable.

Scalability is okay. It's easily scalable. We don't have any plans to increase usage at the moment. We currently have two users directly using this solution. Indirectly we have around 50 users.

We require two staff members for maintenance and others are just consuming data from it.

There hasn't been a need to contact technical support at this point. We haven't had any technical issues. 

The initial setup was straightforward. The deployment took a few hours. 

We integrated it ourselves. 

We have seen ROI. It's been useful.

It's around $200 US dollars. There are some data transfer costs but it's minimal, around $20.

I would rate it a ten 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.

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.

• 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.

Columnar storage technology is the most valuable feature of this solution.

We can get the SLS/SLAs in our daily processes.

Some improvements can be brought about in:

Restore table:

I would like to use this option to move data across different clusters. Right now, you can only restore a table from the same cluster.

Right now, the feature only permits bringing the table back in the same cluster, based on the snapshot taken. I would like to have a similar option to move data across different clusters, right now I have to UNLOAD from cluster A and then COPY in cluster B. I would like to use the snapshots taken to bring the data in the cluster I need.
Maybe current design cannot be used, because it is based on nodes and data distribution.

But, our real scenario is: if we lose the data and we need to recover it in other cluster, we have to do:

1) Restore table in current table with a different name

2) Unload data to s3

3) Copy data to a new cluster. When we are talking about billions of records is complex to do.

Vacuum process: The vacuum needs to be segmented. For example, after 24 hours of execution, I had to cancel the process and 0% was sorted (big table).

Vacuum process:

The vacuum needs to be segmented, example after 24 hr of execution, I had to cancel the process and 0 % was sorted (big table)"

For big tables (billions of records). if the table is 100% unsorted, the vacuum can take more than 24hrs. If we don't have this timeframe, we have to work around taking out the data to additional tables and run vacuum by batches in the main table.

Why, because If I run the vacuum directly over the main table, and I stop it after 5 hrs, 0 records will be sorted. I would like to run the vacuum over the main table, stop when I need but get vacuumed some records. Like incremental process.

I have used this solution for around three years.

We did encounter stability issues, i.e., if you are using more than 25 nodes (ds2.xlarge), the cluster is totally unstable.

I have not experienced any scalability issues.

I would rate the technical support a 9/10 for normal issues.

However, for advanced issues, I would give it a 5/10 since I had to go directly with the AWS engineers support.

Initially, we were using the Microsoft SQL solution. We decided to move over to this product due to the DWH volume and performance.

In my opinion, the setup was normal.

Based on quality of the product and its price, it is the one of the best options available in the market now.

We also looked at the Oracle solution.

You need to make sure that the space used in DWH has to be a maximum of 50% of the total space.

You must create processes to vacuum and analyze tables frequently. Also, before creating the tables, you should choose the right encoding, DISTKEY and sort keys.

The most valuable features of this product are:

• Processing huge data in petabytes
• Massively Parallel Processing (MPP)
• Concept of data compression
• The way it stores the data in drives especially with the distribution key
• Supports BI tools like MicroStrategy (MSTR) and Tableau
• Supports all the data warehouse core features such as SCD1 and SCD2, and different schemas like the star schema

It has helped us to understand the response and interest of the customers and the user conversion rate in this competitive world. Thus, it has helped us in the decision-making process.

In most of the scenarios, the data source for Redshift will be traditional RDBMS like MySQL, PostgreSQL, SQL server, etc. After migrating to Redshift, we will find few disconnects for w.r.t data types, the stored procedures and other complex functionalities. There is a need for improvement in these aspects, mainly in the scope of data types and some complex functionalities which we can perform in RDBMS.

I have used this solution for more than a year.

I have not encountered any issues with stability. In terms of performance, Redshift is highly stable.

I have not encountered any issues with scalability. We can easily scale the nodes in AWS only with a few clicks.

I would give the technical support a 6 out of 10 rating.

We have not used any other solution.

The setup was straightforward for those who know AWS.

The Redshift pricing policy is easy to understand.

We did not evaluate other options prior to selecting this solution.

As of now, Redshift is far better than the other products in the market.

Lastly, I would like to mention that Redshift is more about scaling and stabilizing your data. One should also focus on data modeling from time to time.

Our data warehouse is Redshift now.

Overall, I'm satisfied with the solution so far, and, from an accounting perspective, it works well for my tasks and duties. 

The product is very stable. 

Changing from local servers to the cloud is very easy. It's so nice not to have to worry about physical servers. 

We need more AWS applications. They have some solutions from Amazon that can manage performance, however, we will need something that can also manage financial reporting, visualization, and analytics - instead of having to go to other solutions like Tableau or Power BI. If they can offer comparable options, we'd like to be able to choose all AWS solutions instead of other platforms. 

The refreshment rate of data reaching Redshift from other sources should be faster.

It could be more efficient during tasks such as data refreshing or uploading. 

I've been using the solution for about a year or so. It hasn't been too long just yet.

The solution is very stable. It's better than OWC, as a technology. There are no bugs or glitches. It doesn't crash or freeze. It's reliable.

I cannot speak to how helpful technical support is as this is not my area of expertise here. I'm not directly connected with AWS. That said, it is my understanding that they are doing a pretty good job with our own technical team and they have been helpful.

We used ODBC before to maintain our tables. Oracle ODBC was used as a server, as a data warehouse environment.

Changing to Redshift was a big change for us. However, after a while, we get used to it and it is okay now as it's coming together under a bigger picture of a framework, as a bigger framework. We are going to keep everything in the cloud and moving to AWS and we will put everything there and manage it as a framework going forward.

I'm not using it day in, day out and I did not handle the initial setup. There is a team involved with that side of the product. I'm using it for the closing of the accounting cycle every month. I don't handle implementation tasks. 

I cannot speak to the pricing. I don't handle billing or payments. 

We're customers and end-users.

While I cannot speak to the exact version number, my understanding is that we are using the most up-to-date version of the solution. 

I'd advise those considering the solution to go for AWS products. They are the best. You will have more synergy between your software. It's nice not having multi-software working on the technology itself. If you can, use as many genuine Amazon products as possible and integrate them together. 

Overall, I would rate the product at a seven out of ten.