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 …
Starting at $0.24 per GB per month
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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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- No setup fee
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- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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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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April 02, 2021
Amazon Redshift Review
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.
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
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 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.
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.
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.
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