Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
$0.24
per GB per month
SAP Business Data Cloud
Score 8.6 out of 10
N/A
SAP Business Data Cloud is a fully managed SaaS solution that unifies and governs all SAP data and seamlessly connects with third-party data—giving line-of-business leaders context to make even more impactful decisions.
Amazon Redshifts has fewer features but at the same time, you also have some gains once it is running on AWS Cloud and it is really easy to set up. Besides that, in our case, it is a bit cheaper and we don't really need the extra features that you can find on Snowflake. Another …
Amazon Redshift, BigQuery, and Snowflake are all fully managed data warehouse services that are designed to handle large volumes of structured data and support business intelligence and analytics efforts. However, Amazon Redshift has the upper hand with its cost-effective …
Biggest advantage of Amazon Redshift is it's part of the aws ecosystem. When tuned well it is also very cheap compared to something like snowflake. And compared to spark or databricks, Amazon Redshift is a solid warehouse that's well suited for tabular data. We use it for user …
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a …
Redshift leapfrogged Hive back when Hive was trying to figure out how to implement indexes, providing a more stable, standardized (postgres), easy to use (any postgres client), easier to administer, and scalable solution for querying server logs and raw usage data.
Amazon Redshift is one of the fastest service offerings available in the market now. Plus you get an advantage of using a cutting edge compute service offering from AWS. Other technologies are fast but not as good as Amazon Redshift, I would say. Our business is interested in …
1. Redshift has better compression (automated) consuming less space then competitors 2. Automated Vacuum Delete for having consistent performance 3. AWS introduced ra3 node types for simple separation of compute and storage
Its definitely an improvement on all fronts for our business needs. Again, our MySQL server was really slow and we needed a more efficient solution. It was a major upgrade, but it is much more expensive than an in house server. It was expected but I'd say that lots of headaches …
Amazon Redshift supports multiple data formats including multiple structured data formats. And it is easy to implement a cluster if you do not have knowledge of data lake solution. Also when you do not need a lot of resources, you can just scale down so you do not have to spend …
Amazon Redshift has a better UI, hands down. And it is easy to integrate with bigger tools like Talend. It has many issues when it comes to understanding the architect perspective like Toad, which has a better UI for architect data together. However, that is because we are not …
We like Snowflake for its separation of computing and storage and also the separation of data warehouse different users. We replaced Redshift with Snowflake. However, Snowflake is great for its pay for performance kind of methodology.
Azure SQL Database was discarded because of a less attractive licensing, costs, plus its integrates poorly with many of the Azure offerings as say Azure Data Factory - it is not a true ETL yet. Also, the rest of the tools used were of Open Source type and it did not look like a …
It works on the cloud and we use the platform Dbeaver which is very unique and easy to maintain. There are very limited tools of this kind but the security issues are pretty high within those tools.
As our applications are hosted on AWS service, Redshift is the best option for us. Also, it provide a near to real-time performance on limited datasets and less complex queries. High availability is the major concern for any growing business and AWS is the best option for this. …
We are currently on Redshift, because it was out before Snowflake. However, Snowflake looks promising. It's the new shiny toy that gives options that Redshift does not provide for. The big thing is that storage and compute can be scaled separately, whereas you cannot do that in …
Most of our stack is on AWS, so while Snowflake and BigQuery was a viable option from a performance perspective, it was easier to integrate with RedShift. We considered hosting SQL Server on AWS or using Amazon RDS (Postgres or MySQL), however, the self-service aspect of …
Snowflake supports semi-structured data types and provided solutions to manage/process the semi-structured data. It supported sharing data between the different accounts and makes it easy in the scale and scale down process. Snowflake doesn't limit users on the database.
Amazon Redshift is much easier to set up and start using. It interacts well with the PostgreSQL client (psql) and shares certain basic data dictionary, and people familiar with PostgreSQL feel right at home. The cluster is part of AWS services offering, and it works well with …
Some organizations use PostgreSQL as an OLAP store. PostgreSQL offers a modern SQL dialect, data types, and features that Redshift lacks. RDS is a great managed PostgreSQL product. However, PostgreSQL is a poor choice for a data warehouse. It's row-oriented storage requires …
The reason is pretty straight forward. Majority of our data originates from SAP source systems like SAP S4 private cloud, S4C public cloud and Bydesign. So SAP Business Data Cloud stands tall when compared to other solutions and i honestly believe this is best suited solution …
With a S4 backend a lot of core functionality is made simpler - authorization, data types, currency conversion. In particular if the front end choice is SAP Analytics Cloud.
The lack of a good connection from Power BI to the datasphere application (instead of the underlying HANA …
Business data cloud is one tool that takes care of extraction, modelling, and AI ML use case in the landscape, whereas in other tools I was using multiple services to take care of each of the functions, hence BDC is a cool tool im action for our use cases. Low low-code option …
BDC is a significant development over Datasphere in the sense that it unifies not only the transaction and olap systems but also other olap systems which may have existed in the landscape. Pce migration and availability of data from and 7.5 or B4 HANA system as a data product …
If the number of connections is expected to be low, but the amounts of data are large or projected to grow it is a good solutions especially if there is previous exposure to PostgreSQL. Speaking of Postgres, Redshift is based on several versions old releases of PostgreSQL so the developers would not be able to take advantage of some of the newer SQL language features. The queries need some fine-tuning still, indexing is not provided, but playing with sorting keys becomes necessary. Lastly, there is no notion of the Primary Key in Redshift so the business must be prepared to explain why duplication occurred (must be vigilant for)
SAP Business Data Cloud is suited mainly for SAP data integration. We could able to easily consolidate the data from S4 and service cloud V2 system. SAP Business Data Cloud enables realtime data replications. We could able to leverage the AI core features. As my previous data warehousing skill is from SAP BW, I am missing some basic features comparing to BW. Master data manual maintenance, Time dependant masterdata, language independent text is also not straightforward.
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.
SAP Business Data Cloud help in arranging data at one single place reducing cost of maintenance for multiple platforms and governance.
Once data is arranged , it can be modelled as needed using Datasphere and in data bricks for AI needs
Data fabric provided in SAP Business Data Cloud helps in reducing man efforts to design models needed for PNL reporting as data fabric helps in quickly designing the solution with great accuracy.
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.
The platform provides strong values in terms of centralized data management, scalability, and integration with the SAP ecosystem. It supports enterprise reporting, governance and analytics effectively. While there is always room for improvement in usability and cost transparency, the long term benefits in performance, data consistency and cross functional collaboration and expanding within the organization
Overall it serves all our aspects of data management like data cleaning, data manipulation, and data reporting on the cloud platform. We can create stored procedures and triggers in it very easily as all the options are self suggested in it. We can easily attach the results of ARS to the other tools as well for drawing the statistical results.
SAP Business Data Cloud offers robust capabilities that enable me to analyze data and extract valuable business insights. It creates a single source of truth with seamless SAP integrations that enable faster, more reliable reporting. It has freed my team from complex engineering efforts by simplifying data analytics, giving us more time to focus on generating insights.
The support was great and helped us in a timely fashion. We did use a lot of online forums as well, but the official documentation was an ongoing one, and it did take more time for us to look through it. We would have probably chosen a competitor product had it not been for the great support
support team is generally responsive and knowledgeable, and most issues are addressed within acceptable timelines. Documentation and standard guidance are helpful for common scenarios.
One of the best training session I attended and they covered most of the topics and answered all our questions. participants joined from different regions, infact they all had a different questions and it was different thoughts from all of then and helped to learn better. Though I was on travel, I could able yo attend the session.
I have done implementation of models in traditional bw and Using BDC. The integration of BDC with S4 hana for creating sap data products is seamless and reduces lot of implementation effort. The intelligent app feature is BDC also eases the implementation effort. If i have to compare the previous world with new BDC, implementation effort is largely saved
We evaluated [Amazon] Redshift vs BigQuery vs Amazon EMR, back in 2014. Back then BigQuery cost was slightly higher than that of [Amazon] Redshift price structure. Amazon EMR, needs lots more management (Admin tasks) and EMR is designed to be ephemeral and not designed to be a data store. [Amazon] Redshift was ideal with the price structure, performance and ROI[.]
The reason is pretty straight forward. Majority of our data originates from SAP source systems like SAP S4 private cloud, S4C public cloud and Bydesign. So SAP Business Data Cloud stands tall when compared to other solutions and i honestly believe this is best suited solution for the warehousing and reporting to our organization.