The HCL Actian Data Platform (formerly Actian Avalanche) hybrid cloud data warehouse is a fully managed service that aims to deliver high performance and scale across all dimensions – data volume, concurrent user, and query complexity – at a lower cost than alternative solutions. Avalanche has built-in self-service data integration that can be deployed on-premises as well as on multiple clouds, including AWS, Azure, and Google Cloud, enabling users to migrate or offload applications and data to…
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Amazon Redshift
Score 8.9 out of 10
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Amazon Redshift is a hosted data warehouse solution, from Amazon Web Services.
VectorWise is suitable to be a departmental data mart database or an operational data store (ODS). It is not suitable for enterprise data warehouse database.
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)
The support community was not as robust as you would find in a Mulesoft or Informatica environment. Given time and growth, it’s possible it will blossom, but for now it is minimal.
Training is always a big thing for us, and the tool was not expansive enough for us to implement our own internal training program. There was some online training, and we acquired an expert when we brought on the new company, but some additional training tools would have helped the tool grown its user base internally.
Not a lot to set it apart from the competition. Most of the features are available with other more established tools, but for a small company that maybe grew too quickly and needs to get its arms around many different data sources, I can see the appeal. Not really geared for larger firms.
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.
As I said before, more training or greater visibility to training tools/options would be a plus. It’s easy to publish YouTube videos these days, I think they should make more of them.
Differentiation would help, there’s not a lot out there to drive you to buy the product if you are well informed in the market. If you know the market, you steer towards the large or trendy products. It’s a good product, but lost in the noise of the field I think.
Hitching the wagon to a major software brand (like Mule did to Salesforce) would help grow the user base, and thus increase the activity in the support community. More users also translates into product champions.
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.
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.
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
We didn’t actually choose Actian, it arrived as part of an acquisition, and really served its purpose both when it was used by the smaller firm we acquired as well as afterwards when we were extracting data and folding the company into our own data and analytics culture. The included hundreds of pre-built connectors gave us lots of options, but in the end, we were just too large of a company to rely on the product and needed a big-name player to address our wide-ranging needs. Powerful for its size, but not sized enough to address big businesses.
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[.]
