Products that are considered exceptional by their customers based on a variety of criteria win TrustRadius awards. Learn more about the types of TrustRadius awards to make the best purchase decision. More about TrustRadius Awards
Leaving a video review helps other professionals like you evaluate products. Be the first one in your network to record a review of Amazon Redshift, and make your voice heard!
Redshift Managed Storage
$0.25 - $13.04
$0.25 - $4.08
Entry-level set up fee?
- No setup fee
- Free Trial
- Free/Freemium Version
- Premium Consulting / Integration Services
- Ease of setting up ETL
- Uploading data into Redshift via AWS
- Querying is quick
- Missing option to restrict duplicate records
- Lacks complex data sets like udf
- Does not offer UI based querying & visualisation option like Looker
We also use Amazon Redshift Spectrum as our Data Lake, where we store raw (un-processed) data (Cold data) for historical analysis, trends, etc
We store various standard data in Redshift like:
Bronze (ETL-ed data),
Silver (Materialized Views data), and
Gold (Rollups/Aggregated/Dashboard-ready data) in [Amazon] Redshift
- [Amazon] Redshift has Distribution Keys. If you correctly define them on your tables, it improves Query performance. For instance, we can define Mapping/Meta-data tables with Distribution-All Key, so that it gets replicated across all the nodes, for fast joins and fast query results.
- [Amazon] Redshift has Sort Keys. If you correctly define them on your tables along with above Distribution Keys, it further improves your Query performance. It also has Composite Sort Keys and Interleaved Sort Keys, to support various use cases
- [Amazon] Redshift is forked out of PostgreSQL DB, and then AWS added "MPP" (Massively Parallel Processing) and "Column Oriented" concepts to it, to make it a powerful data store.
- [Amazon] Redshift has "Analyze" operation that could be performed on tables, which will update the stats of the table in leader node. This is sort of a ledger about which data is stored in which node and which partition with in a node. Up to date stats improves Query performance.
- Amazon Redshift is a Managed Service. But it is Not a 100% managed service. We still need to configure it with WLM (Work Load Management) settings, and add Query Queues to make sure it's resources aren't wasted and it is performant at it's best state, all the time
- [Amazon] Redshift has a concept of "Vacuum", which is an operation to claim the disk space back from deleted data/tables. They recently started doing automated vacuuming. Prior to that we had to do that at regular intervals, to claim the data back.
[Amazon] Redshift might not work 100% well with full performance, for Graph DB use cases.
Amazon redshift is an integral part of our data analysis.
- Amazon redshift is super quick due to its Massive parallel processing
- Amazon Redshift is compatible with many visualization tools which helps visualize the insights
- Amazon redshift has almost 0 downtime and allows for a massive store of data
- Since Redshift is a part of a larger AWS ecosystem, connecting with other resources is never a problem
- Amazon redshift could have more detailed documentation including practical examples
- Amazon redshift still lacks some of the advanced concepts which are possible with MS-SQL and others
- It should have a feature where users can visualize the data stored for a better understanding
for eg. you can use amazon redshift to gain insights from a large data set but cannot use it to do a transaction level update and insert
- Data integration is very simple to perform
- The tool provides some advice that is very useful
- Their support is always complete and easygoing
- Their documentation could be even better
The eventual product: a Bill Inmon principles-based Data Warehouse served as a point or source of a single truth. It aided in decision making, historical outlooks and forecasting across various organizational verticals - the Finance, Marketing, and Medical Research. It was also possible to deliver data extracts to 3rd parties or visualize data on demand.
- Data retrieval experience really gets improved.
- In terms of database management, it is really a no management at all in AWS. There is no even an OS to take care or worry about.
- Auto or on-demand scaling is nice.
- Integrates quite well with other products within the AWS ecosystem.
- The number of connections is too small, I think at around 50 are allowed in parallel. With some ETL and apps connecting all the time, this brings an undesired possibility to some users or tools being unable to connect.
- Needs some tuning.
- The logging part is almost nonexistent.
- Can be quite costly in the long run as opposed to just RDS or on-prem/dedicated solutions.
- Robust as compared to traditional database/data warehouse
- Offers significant query speeds
- Low cost of ownership
- Provides MPP only for AWS-supported storages
- Prerequisites for configuring tables are not easy
- Not great for use with web apps
- Large scale SQL
- Standard SQL
- Handline full text queries
- Bonafide indexes
- Provide query interface that can store queries and run long-running queries, then notify the user
- Redshift has concurrency scaling helps serve more customers queries
- Redshift has automatic table compression having less disc space consumed comparing to other data warehouse solutions
- With ra3 new node types we can separate storage and compute
- Having automatic vacuum delete helps having conzisent performance in cases where data variability in dwh production zone is present
- Consistent service improvements from AWS: temporary tables, null handling in joins, single row inserts, materialized views
- Frequent changes of management console look and feel
- Automatic vacuum sort doesn't work for several billion rows tables
- Disc IOPS performance monitoring excluded
- Extracting data
- postgres Based
- Could be faster
- Limited sql workbenches
- Expensive when speeding up the processing
- It is very powerful, can hold anything you have.
- It is scalable. Small or big, it can help you.
- It is very fast. Can spin up cluster in minutes.
- As data warehouse, it does not support fast I/O.
- Learning can take more than expected.
- You are not managing your data 100%.
- Great UI
- Implementation in non AWS server
- Replication is excellent, we did not have to worry about reliability.
- Their auto-scaling feature came to our rescue when it came to cost management.
- It became expensive over time as the data increased over time.
- It could not separate users from using the same infrastructure.
- The initial costs were very low, and it was super easy for us to spin up databases. Also, setting up multiple instances were easily managed with just a click of a button.
- The costs increased gradually, and it became more expensive than planned. The query analyzer was not up to the mark and needed constant support from the AWS support team. Gladly they were willing to help and had a good experience with them.
- Fixed cost.
- Tunable table design.
- Need to provision warehouse for highest capacity.
- No real separation between computing and storage (even when considering Spectrum).
- All users share the same infrastructure resulting in frequent 100% utilization error messages.
- A leader node can become a bottleneck for too many concurrent aggregate queries.
- Since it's part of AWS it is fairly quick and easy to set up.
- You can add nodes fairly quick to expand the data needs.
- Performance from the analytics reports accessing Redshift is really good.
- Better database management when looking up table metadata or sizes of tables.
- Need a better query analyzer.
- Finding errors during a data load can be a little daunting at times.
- We can connect with multiple servers and can fetch the data easily from one server to other.
- It supports the syntax of the bots of the SQL servers, MS SQL and Oracle SQl. This makes it pretty handy to use.
- Here we use views instead of tables, so we can clearly see the flow of data.
- It works very slow in the cloud environment.
- No statistical inbuilt functions are available within the tool.
- Its user interface is not very attractive.
- Often it goes into deadlock state, which kills the running jobs.
- Easy query and fast execution
- High performance and availability
- Support of large datasets
- Scalable solution
- Database optimization
- Time consuming process for schema design and modification
- Integration is little bit difficult
2) All Department use it—Engineering, Sales, and Marketing.
3) As I said data is almost in real-time, so it is very useful for taking real-time decisions for upper management. We also reduced Salesforce licenses, because most of the users only used it to see reports. Now they are happy to used Redshift.
- We reduced the number of Salesforce licenses— Engineering, Sales and Marketing guys are happy to query data from Redshift.
- Very fast to provide a huge data set with complicated measure.
- Some of the calculations failed in Salesforce. Redshift returns with the same calculations very fast.
- Very easy to maintain, no need to worry about hardware failure.
- We are not able to modify column size.
- It's fast for data analytics across multiple columns.
- Essentially, it's good for big datasets.
- By using RedShift you're kind of married to using AWS's other services, e.g. Redash.
- You need your data in the cloud.
- No separate storage and computing.
- No structured data types.
- Doesn't scale easily.
- Complex queries
- Fully managed service
- Works very well with most BI/reporting solutions
- Stored procedures
- Job scheduling
- A easier way (perhaps a GUI) to manage users permission
- User-experience. The user wants something quick to view the output, rather than spending too much time
- preparing a code prior to seeing the output. Redshift provides SQL type queries. This makes any user happy and comfortable.
- Architecture is very straightforward and simple to understand, such as MPP architecture, Encryption, and Columnar database design. We can easily address issues and help others to understand.
- Scalability. We can scale-up and scale-down based on our workloads.
- Performance tuning and database optimization can be done using the system tables and advisors. These solutions are similar to the solution available for Oracle SQL Server. It makes it easy to do the optimization for queries and databases.
- The concurrency and scale up based on it could be improved. It would be good if it scale-up and scale-down the memory/CPU capacity automatically based on workload.
- Often we experience slow on queries and dashboards. Self-tuning option in WLM does help.
- Optimizing the areas such as Vacuum and reorganize the column data (sorting over time) automatically.
My company used it solely for reducing the performance overhead of running long SQL queries. The seamless implementation of Redshift allowed us to get the data ready to go for our customers to run the reports they need. It is currently used by a few customers, but we are trying to get each of our customers to use this rather than using the traditional OLTP database.
- Easy to work with
- Seamless implementation with matillion
- Massive data reads and inserts
- I didn't like the security aspect of this where it asks us to create views for each customer.
- It does not support row-level controls.
- Some SQL queries are faster on native SQL than here. But it could be the data conversions that is causing it.
- Extremely fast querying allowing for concurrent analysis.
- PostgreSQL syntax which allows for developers with a SQL background to easily begin working with the data.
- Multiple output formats including JSON.
- Safe, easy, and reliable backups.
- SQL syntax support is not 100% which can lead to frustrating situations when developing a query.
- No support for database keys.
- No stored procedure support.
Not appropriate for a transactional system (though this is not what it is built for obviously). Must keep in mind the data you are syncing up to the cloud and scrub if necessary before. Something to always be mindful of of course.
- It does very well in data ingestion, and compresses data efficiently.
- Most of the queries return results quickly even with large data sets.
- It has a hard limit of total number of concurrent connections to the database. Compared with conventional databases that limit is very low.
- Its workload management (WLM) mechanism could be improved, such as made more dynamic and easier to tune and manage.
- If you need draw insights from immense amounts (see: petabytes) of transactional (repetitive) data in near real time--think machine learning and business intelligence--and you're already in the AWS ecosystem, then it's your only real option. It performs very well.
- Highly configurable, intelligent compression of repetitive columns reduces your memory footprint, lending to extremely high performance.
- As with most things in the AWS ecosystem, it scales seamlessly and endlessly.
- There is no support for data de-duplication; meaning this has to be either accounted for upstream, or you'll have to build your own services to de-dupe your data.
- It's strength is housing data, not necessarily data insertions. While it has an SQL-like interface, it shouldn't be approached the same as a typical relational database.
- Permissions can be a pain... dovetailing on my previous "con" , in some instances it's easier to drop/rebuild a table than try to navigate incremental updates/insertions, but retaining user-permissions is a pain-point.
- Petabytes of data requiring near real-time analysis
- Massive Data Insertions
- Massive Data Reads
- Web apps
- Smaller transactional inserts
- Smaller reads
- Redshift is fully managed. Small teams do not have the resources to maintain a cluster. CloudWatch metrics are provided out-of-the-box, and it is easy to configure alarms.
- Redshift's console allows you to easily inspect and manage queries, and manage the performance of the cluster.
- Redshift is ubiquitous; many products (e.g., ETL services) integrate with it out-of-the-box.
- Writing .csvs to S3 and querying them through Redshift Spectrum is convenient.
- We've experienced some problems with hanging queries on Redshift Spectrum/external tables. We've had to roll back to and old version of Redshift while we wait for AWS to provide a patch.
- Redshift's dialect is most similar to that of PostgreSQL 8. It lacks many modern features and data types.
- Constraints are not enforced. We must rely on other means to verify the integrity of transformed tables.