Amazon Redshift vs. SAP Business Warehouse

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)

Incentivized

SAP BW is best for: 1. Large enterprises 2. Enterprises with 3+ legacy systems with entrenched users (politically difficult to merge) 3. Enterprises with employees who can understand both the technical capabilities of SAP BW and the needs of the business users - ability to speak both languages, otherwise the program could be unwieldy and potentially underutilized (it's not particularly inexpensive) SAP BW is less appropriate for: 1. Small enterprises 2. Enterprises who have well established, same location, CRM and UFS - the integration of data analysis will be easier and less expensive with other solutions 3. HANA

Incentivized

• 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.

Incentivized

• ETL, great at taking data from OLTP source, flat file, etc... Easy drag and drop for transformations.
• Great reporting possibilities, easy to connect with BO, BPC, etc...
• Great Master Data Management (MDM )for BPC - helps us maintain master data for BPC.

Incentivized

• 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.

Incentivized

• Age of software is showing as it struggles with very large data modules, which were not as prevalent in its early years
• Querying performance at times can be very slow
• Support and development for BEx has been discontinued or hard to find.

Incentivized

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.

Incentivized

The overall usability is robust. The tool offers lot of native feature to achieve all the data warehousing functions. Starting from data modelling to reporting and authorisation, the tool provided native features for almost all the areas of analytics. Integration of hybrid modelling with hana studio opened the usage of sql functions with the sap analytics

Incentivized

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

Incentivized

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[.]

Incentivized

SAP Business Warehouse scores higher in data warehouse functionalities for integration to SAP ERP and other SAP solutions such as SAP CRM, SAP APO, and SAP SRM. Standard SAP data source extractors which are available in SAP ERP can be used immediately for full or delta replication into SAP Business Warehouse. System governance in SAP Business Warehouse is top-notch with change management support for migration between system landscape from the development system to production system.

Incentivized

• It allows for an almost seamless integration of our data which can then be used by other departments for analytical purposes.
• No in house resources are needed for keeping the data alive and performing backup/migration tasks of the data in its end state.

Incentivized

• It has helped cut down on parts shortages with the minimum stock flags.
• People are able to track down parts easier because they can see where they were last used.
• Our sales team has better visibility on lead times so they can accurately quote our customers.

Incentivized