Give a chance to Amazon Redshift (It worths)
We are using Amazon Redshift as our main data warehouse to store most of our data, the whole process consists in extract the data from …
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What is Amazon Redshift?
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
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Redshift Managed Storage
$0.24
Cloud
per GB per month
Current Generation
$0.25 - $13.04
Cloud
per hour
Previous Generation
$0.25 - $4.08
Cloud
per hour
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ETL From Amazon RDS to Amazon Redshift with using AWS Glue Service
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Introduction to Query Scheduler for Amazon Redshift
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ETL From AWS S3 to Amazon Redshift with AWS Lambda dynamically.
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Amazon Redshift Tutorial | AWS Tutorial for Beginners | AWS Certification Training | Edureka
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What is Amazon Redshift?
Amazon Redshift Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
Frequently Asked Questions
Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
Reviewers rate Usability highest, with a score of 10.
The most common users of Amazon Redshift are from Mid-sized Companies (51-1,000 employees).
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August 09, 2021
efficient, performant data store
Amazon Redshift is our Data Warehouse, where we store our processed data (Hot data) for various initiatives like BI, Analytics, DataScience, etc
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
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.
We have decided to purchase Amazon Redshift since we started the project of building a new "data lake," so the first step was to decide which tool would be more appropriate to use as a data warehouse. Since we have everything on the cloud, we choose Amazon Redshift to connect our current tools on AWS and integrate the data.
- 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
January 20, 2021
Fly into the data warehousing world of AWS with Redshift
We use Amazon Redshift for our insights platform in our R&D space. Our team creates reports and dashboards on tools for business use. Amazon Redshift provides greater supply chain visibility, increased information on product movement, and high efficiency at a much faster rate.
- 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
December 30, 2020
Redshift - it is not the destination it is journey !
Cloudwalker offers analytic services for the gambling industry. The gambling industry has vast amounts of data that are high speed and variability. Our services from Redshift help gambling companies have better control of their bookmaking product, have a complete view of customers betting history, helps with detecting problematic accounts, etc.
- 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
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
November 30, 2019
Redshift Review
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.
September 27, 2019
Redshift is way too easy!
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.
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 13, 2019
Great Analytic Data-Warehouse
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
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.
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.
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.
November 26, 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.
October 12, 2018
A hands-free data warehouse
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 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.
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
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.
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