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Filter 137 vetted Amazon Redshift reviews and ratings
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February 26, 2020
Amazon Redshift: Fastest but comes at a price
We are performing a POC (proof of concept) across multiple cloud vendors, and we have evaluated GCP, Azure, and Amazon. We plan to go on-site cloud from on-premises, and we will evaluate all databases across all cloud providers. We are making the current database over Amazon Redshift, if it can help us do the exact same job as we have on-premises SQL Server. The on-prem option is good for replication and security, and we are evaluating how good it can be on ML application support.
- Robust
- Great UI
- Price
- Implementation in non AWS server
My team is handling all the marking activities for all different product verticles for our company. We get a lot of streaming data that we focus on marketing. We also store this data carry out annual audits and aggregations to analyze our marketing progress. Amazon Redshift helped us with this, and it was a breeze enabling it in our environment as we were already on AWS.
- 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.
Amazon Redshift is a PostgreSQL based solution was seen as a drop-in replacement for several Postgres based databases (or schemas in Postgres parlance).
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.
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.
Amazon Redshift was our enterprise data warehouse as a backend to our BI solutions.
- 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.
Redshift is our data warehouse used by our organization. It takes data from different sources and put them together in Redshift for our Analytics team to diagnose.
- 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.
Amazon RedShift is used in all departments and accounts. This tool is newly integrated into the system so as to work with the data on the cloud. There are various projects which are moving from SQL servers to ARS because of its capacity of working and managing the data in the cloud.
- 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.
1)ETL(Talend) data from source applications (Salesforce, Jira, OpenAir, NetSuite, Sharepoint, Active Directory, Office 365, etc.) to S3 bucket and from S3 bucket push data to Redshift (sync time is 10 minutes intervals. Data is available almost real-time only lagging 10 minutes).
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.
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.
June 08, 2019
Amazon Redshift: easy, simple, fast
Capital One has many LOBs (line of businesses). I have supported IAM and Commercial LOB. They are using Redshift as a data warehouse solution. Oracle is not a Data Warehouse solution but was being used when the application was on on-premises. When they wanted to migrate all data to the Cloud, they chose Redshift as a solution to move the data from Oracle. Oracle is not a data warehouse solution. Redshift has been found as a good solution because of its unique features such as its MPP architecture, columnar architecture, and storage capacity.
- 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.
Amazon RedShift is used primarily by our in house data analytics and psychometric department. When we process the results of physician board certification exams we have a workflow which integrates the newly processed scoring data into RedShift which is used for analytical purposes. The data is denormalized and stored in a fashion which makes it overall more optimal for querying purposes. This data serves as the basis for final scoring calculations for a given exam. The data analytics department is then able to run their statistical analysis against the housed sets of data to ultimately determine the final test scores. We also house an "item bank" of exam questions from past exams which are used as the basis for future exams. Scoring is correlated to the item bank to help determine which questions performed well and which ones exam takers had difficulty with. RedShift is a great tool for our IT department because it helps share the responsibility of having to secure such sensitive data. Amazon is a company which we feel very confident with and RedShift has proven to be extremely robust and fast. It is nice to be able to so easily perform backups of data and rest more assured that it is in a safe form and not something which our own IT department has to manage along with all of our internally hosted transactional databases.
- 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.
AWS Redshift is the cloud-based data warehouse where we store our application level datasets and is used further for insights from the stored dataset. It improves the decision support for our business based on data analytics on a large set of real-time datasets which force the business processes to the next level. It provides good performance with high availability for essential data analysis and valuable intelligence.
- 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
June 15, 2019
Start a data warehouse today with Redshift!
Redshift is being used by engineering for our data warehouse or data lake, if you will. It's part of our ETL pipeline, where the data is used to form dashboards and analytical queries across all of our initially segregated data. So it is kind of a source of truth linking data across the company. These dashboards are accessible across other departments in the organization. The data is consumed by everyone, not just engineers.
- 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.
Amazon Redshift is being used as our primary analytic data-warehouse. This allows our data and analytic team to build report and query data without going directly to our production database. It is a central data repository from external data sources as well, data we import from 3rd parties and segment.
- Complex queries
- Aggregation
- 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
We use it as our data warehouse for reporting purposes.
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.
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.
We use Amazon Redshift and Redshift Spectrum for our data warehouse. Our production transactional datastores are continuously replicated to Redshift and transformed into fact tables. Redshift is maintained by the data team, but it is used by analysts on most teams, including business intelligence, product, and customer support. Redshift is our source of truth; it provides information about business processes that the team needs to make decisions.
- 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.
November 25, 2018
Great for "Big Data" -- but not so much otherwise
Redshift is currently being used to house normalized client data pulled from various third-party endpoints. It houses the data that is both being accessed directly by our business intelligence and CRM platform, as well as made available via our own API gateways. It was chosen for its ability to support a "big data" environment with high availability.
- 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.
December 19, 2018
Amazon Redshift - a new generation of data warehouse platform
Amazon Redshift is being used as the main data warehouse across the whole organization. It provides essential business performance data and valuable intelligence. Different departments utilize it for a variety of operations, and decision makers rely on the data analytic tools built upon its data for tracking and enhancement, wherever needed.
- 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.
We use Amazon Redshift as our data warehouse. We store our most used datasets on Redshift. Some of the data transformations are also made on Redshift but we try to avoid to do the heavy transformation there. Our dashboarding tool sits on top of Redshift and runs queries on every dashboard load. Analysts are using Redshift all day as the main source of data. Data savvy engineers and product managers have read access.
- It is easy to use. SQL is one of the easiest languages to learn.
- Fast. Especially if you use an SSD cluster.
- Scalable. If you need more space for your data or want faster results you can add more nodes.
- It is PostgreSQL. I miss some commands and procedural features.
- It is perfect for analytics purposes, but not fast enough for web apps.
- Too expensive for ETL processing.
Amazon Redshift is being used by several of our clients for analysis of large datasets. In most cases, it is used at a department level, in conjunction with other on-prem and in the cloud data solutions, including data warehouses and relational databases.
- Very fast, parallelized data loading from S3
- Full ANSI SQL support
- Highly scalable
- Columnar storage
- Does not scale automatically. Need to be scaled up/down manually by adding/removing nodes
- Does not have support for row level access control
- Charged based on provisioned capacity - not based on usage
September 13, 2017
After Four Months with Redshift
Amazon Redshift is being used by many business units within our company. It is our new data warehousing platform.
- Redshift seems to be as fast processing a large dataset as it is with a small one. It seems, when the dataset size is significantly increased (10x, 100x, 1000x, etc.), DML queries are often executed within the same amount of time.
- Redshift has a powerful graphical admin tool to monitor the ongoing queries in real time and historically.
- Easily expandable capacity. Automatic snapshots that eliminate the need for managing backups. Simple database maintenance strategies with the VACUUM and ANALYZE commands.
- Abundance of detailed documentation and tutorials.
- It could benefit from adding data integrity and programming tools common to other database management systems.
- Amazon Redshift is based on PostgreSQL 8.0.2. That version of PostgreSQL was released in December 2006. While PostgreSQL was much improved since then, the new features were not implemented in Redshift. Many basic features are missing from it.
- Primary keys can be declared but not enforced. Referential integrity (foreign keys) can be declared but not enforced. UNIQUE and CHECK constraints are not supported and cannot be declared.
- IDENTITY can be declared on a column, and Redshift will put unique values into it. However: IDENTITY values in the newly inserted rows won’t be incremental or sequential. To implement a sequential number, you need to write your own custom code.
- There are no stored procedures in Redshift. We are writing SQL script files, and then parsing and running them one statement at a time from a Python program. This also enabled us to implement execution-time error logging.
- In SQL scripts, to check for the row count of affected rows, a complicated join query against some system tables or views has to be executed.
- Data Control Language (DCL) does not exist. No statements like IF, WHILE, DO, RAISERROR, etc.
- On performance of views… Views do not “pass-through” a query parameter which is a potential problem for performance.
- When selecting against a view with the WHERE clause outside of the view, the inner query of the view will be executed first without consideration for the WHERE clause, and only then the WHERE clause will be applied.
- Certain clauses of SQL work many times faster than other clauses. So be careful and test your statements for performance earlier rather than later, especially if working with a large data set.
- There was a situation when DELETE FROM JOIN was unacceptably slow. Replacing JOIN with the USING clause made DELETE instantaneous.
October 12, 2017
Scalable and cost-effective data warehousing tool
Redshift is one of the most scalable yet simple data warehousing tools. It is not only used within the engineering team but widely used by the business team too. One of the major use cases are to power the dashboards and reports which are in looker. It helped to migrate from the traditional email reports to dynamic dashboards and reports which can be shared to various end users and businesses. It helped in faster data retrieval and can scale well with ease.
- Scalability with less downtime.
- Performance optimization without affecting the business i.e zero downtime during optimization
- Powerful yet simple to use. Very easy to optimize and improve performance during the regular read/write operations
- No need of DBA to operate and maintain Redshift as it is a completely managed data warehousing tool
- Good customer support and will respond in very quick time with clear information and documentation
- Well documented commands
- Can propose better optimization techniques based on the business use case.
- Can provide option to set the upper bound on number of connections to cluster
- Can improve on optimized writes/updates
August 01, 2016
Great data warehouse for the money!
Amazon Redshift is used as the central data warehouse. It's main use is for data analytics and reporting. In addition, it is also used by batch jobs to perform various business functions like email lists of delinquent customers.
- Fast analytical queries. The shared nothing and column oriented architecture makes querying very quick compared to databases like Oracle that are designed for OLTP. Scaling is a synch since you can scale out by adding more nodes.
- Easy table modelling. The only tough decisions you have to make are what your distribution schemes and sort keys are going to be. This is a lot easier than defining partition and index schemes in databases like Oracle or MySQL.
- Not much maintenance. Almost everything is managed by Amazon. The only exception is table vacuuming and analysis. I was able to program simple ETL jobs to perform this.
- Works with pretty much anything that works with Postgres. It's hard to find a tool that it isn't compatible with.
- Lack of enforced constraints (except NOT NULL column constraints). You have to be very careful in your testing to make sure that you aren't duplicating rows.
- No stored procedure support. Everything must be accomplished through ETL
- Write operations are very slow and complex.Native SQL row level INSERT and UPDATE statements take an extremely long time to execute. In order to get around this for external data that needs to be loaded, you have to bulk load the data from a flat file to a stage table, then upsert the data from the stage table to your destination table. For data already present in the database, ELT is the only viable way of transforming the data.
- No good native data modelling tools.
- Random nondescript errors happen occasionally. The error messages are not decipherable and forums will have no clues as to what happened. It is just a fact of life.
- No trigger support.
- OLTP style queries are painfully slow. Don't even think about using Redshift for OLTP...
November 21, 2017
Best Analytics Cloud Data Warehouse Database
We used the Amazon Redshift for Analytics Data Warehousing. It helped to process our various departments in organization like renewals, sales, marketing & finance department to analyze the data very quickly and performance effective with tableau reporting tool.
- It's a columnar data storage architecture and which allows it to particularly run structured data query for reporting very fast.
- We used amazon redshift cloud datawarehouse with Tableau, looker reporting tool and it has perfectly helped our reporting needs for business users.
- Very easy to copy data from Amazon Web Services S3 storage container to Redshift Database with simple copy statements.
- It provides built-in commands to table structure effectively with less use of memory.
- AWS can provide some cheaper options with pre core cpu purchase rather than hourly charges on amazon redshift.
- There are no options for on-premise set-up of the amazon redshift database.
- Limited documentation on best practices for dist key, sort key and various amazon redshift specific commands.
My company uses Amazon redshift to store the majority of our website and offline data. We connect Redshift to dashboard/visualization tools that are used by the entire company. Data availability and ad box analysis is very important to my organization, so having a reliable and accessible storage system is vital.
- It is built out and widely used, which makes it easier to debug and learn.
- Cheap to store and query.
- PostgreSQL makes it easier to query.
- We'd like to get streaming live data.
- Compute and storage are connected, which can waste CPU.
- Loading the data into Redshift can be challenging.
August 10, 2016
Redshift review for the analytics environment
It is used as the analytics data SOT (source of truth). All company data, whether from product, marketing, sales, etc., gets synced to Redshift where it can be easily analyzed by analysts. Redshift provides a good tradeoff between the ability to store a lot of data and perform quick and flexible queries on it.
- Flexible, OLAP queries.
- Fast query time (in the order of seconds for most).
- Standard SQL language.
- Fast ways to insert more data.
- VACUUM is a pain, its unclear exactly how often it needs to be done.
- Redshift has a limit on how many concurrent writes and reads you can do that won't scale to 100s of people using it.
- Redshift lacks some Postgres queries that make some standard SQL operations hard.
August 05, 2016
get started with columnar databases easily with AWS Redshift
(JSON) events emitted from platform/web services are transformed and loaded into AWS Redshift in order to support analysis and reporting for our solution.
- AWS infrastructure and support simplifies maintenance and administration
- familiarity with PostgreSQL makes adopting Redshift as a column store easier
- columnar data store allows for high performance queries on large volumes of data
- there are some situations where having a column store more closely integrated as part of our platform would be better
- AWS costs can add up
- many other (open source) column stores have new and interesting features not (yet) available in Redshift
Amazon Redshift Scorecard Summary
About Amazon Redshift
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
Categories: Data Warehouse
Amazon Redshift Technical Details
| Operating Systems: | Unspecified |
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| Mobile Application: | No |