Altair SLC (formerly the WPS industrial analytics platform, acquired by Altair in late 2021) is designed for data science and heavyweight data processing with the languages of SAS and R. Best known for its SAS language compiler, the software includes advanced graphical user interfaces, robust, high-performance data processing and production-ready application frameworks.
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Azure Data Lake Analytics
Score 8.3 out of 10
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Microsoft's Azure Data Lake Analytics is a BI service for processing big data jobs without consideration for infrastructure.
To better understand the data I work with, I extensively use the data discovery and visualization tools available on my workstations, and I always go for the best. Unfortunately, some other tools does not offer any tutorials for the features, making it difficult and …
Unfortunately, Tableau Desktop does not offer tutorials for the features making it hectic and complicated to analyze data, mainly after implementation. WPS Analytics offers detailed tutorials in form of videos and articles for all features.
I use a lot of data discovery and visualization tools at workstations to help me gain deep insights into data. WPS Analytics doesn't vary much from all the DVD tools that I have listed, I always go for the best.
Azure Data Lake simplifies extensive data analysis. It runs Hadoop, HDInsight, and Data Lakes, and even complex queries run smoothly and quickly. We write queries to transform data and extract insights instead of configuring hardware. It can handle any size job by adjusting the …
Compared to Databricks which we have fully implemented and all teams use, Azure Data Lake Analytics was first pushed on our engineering team from the Data Science group pretty much from familiarity. Once we did a proof of technology, we found it to natively have the better …
We did some research about Alibaba Cloud Data Lake Analytics and even being cheaper than Azure Data Lake Analytics, we decided to go for the second one once we noticed they have more features and better documentation. Another thing we considered during this process was the fact …
ADL Analytics supports big data such as Hadoop, HDInsight, Data lakes. Usually, a traditional data warehouse stores data from various data sources, transform data into a single format and analyze for decision making. Developers use complex queries that might take longer hours …
Both of the products selected are very good at what they do, but data lake analytics is able to bundle everything else within our preexisting data lake, which is a very big [deciding] factor.
For now coders, I would emphasize them to use drag and drop functionality. Otherwise, WPS Analytics is a robust tool for app development and data visualization that offers multiple languages such as SAS, SQL, R, and Python.
For us we have an enterprise of SQL users at all skill levels, and this product is very SQL friendly and extremely fast in creation of data aggregates and analysis. If you are an Azure storage user, considering using Lake Analytics over top of your blob or any other storage just adds complementary services and functions native to your existing architecture.
There's a bit of bias towards cloud with ADL Analytics. Depending upon a company's infra strategy and investment plans, there are some challenges with migration and integeration.
Not worth the time/effort/money if the organization doesn't have "Volume" of data. Cost effective only when daily loads exceed around 1million.
While training materials are available online, Adoption rate - Yet to pick up.
To better understand the data I work with, I extensively use the data discovery and visualization tools available on my workstations, and I always go for the best. Unfortunately, some other tools does not offer any tutorials for the features, making it difficult and time-consuming to analyze data, mainly after it has been implemented. In addition to videos and articles, WPS Analytics provides comprehensive tutorials for each feature.
Azure Data Lake simplifies extensive data analysis. It runs Hadoop, HDInsight, and Data Lakes, and even complex queries run smoothly and quickly. We write queries to transform data and extract insights instead of configuring hardware. It can handle any size job by adjusting the power. Azure's servers, networking, and data entry are fantastic. It provides security and assured data access.