The following document captures the questions and answers from the webinar: Flipping the 80/20 Rule of Analytics, presented by John Thuma.
You can watch webinar recording HERE
For additional questions, please contact Arlene Zaima (Arlene.Zaima@Teradata.com).
|The lines of code you had mentioned, that need to be written and developed in Aster too, but it is being done by the Aster development team instead of individual companies?||Yes, absolutely correct. Aster's SQL, SQL-MapReduce and Graph engines provide lower level API that provide the data scientist and engineers to code their custom algorithms, however Aster engineers created over a 100 pre-built algorithms that are available through simple SQL calls.|
|Is it taking care of unstructured data as well?||In this use case, the data was structured sourced from the Teradata data warehouse, however we have many use cases where unstructured data in integrated, transformed and incorporated into the analytic process.|
|Does the new concept of data lakes make any difference to capabilities you show here?||We consider the data lake as another data source as the data warehouses, twitter, etc. Aster has connectors into data lakes such as Hortonworks and Cloudera along with other 3rd party databases, where data can be integrated into Aster for deep analytics. The beauty of Aster is the ability to build powerful analytics with a little SQL knowledge. You don't have to deal with data partitioning, parallelism, and building the algorithms. And as John pointed out, it's competely reuseable!|
|Does the concept of data lakes provide and benefits to the capabilitiesyou have shown here?||Yes, you can build the same analytics wiht a data lake, but the effort and skills sets required are very different. Data lakes require MapReduce, deep machine learning and Java expertise. As you can see from John's slides 16 - 18… the coding footprint is dramatically reduced with Aster. Aster also provides an easy way to promote your analytics into production via AppCenter.|
|Do you have a limit on the data size?||Aster is based on an MPP architecture so we do not have a data size limitation. Aster analytics are designed with scalability at it's core to maximize the parallel processing across the MPP architecture. To maximize efficiency, we've created a SQL, SQL MapReduce and Graph (Bulk Synchronous Parallel processing) engine. Also Aster Appliance includes Hadoop nodes to create a data lake for lower cost bulk storage of data. So you have the best of both worlds: Lower cost bulk storage with a powerful analytics engine.|
|How is it better than other platforms like SAS?||On the surface, it may appear that Aster and SAS provide similar capabilities, however as we drill into it's capabilities, they are very different. We consider SAS as complementary technology that can be used with Aster. SAS is a general purpose analytic tool that provides an business interface for BI reporting and traditional analytics including data mining technology on data that fits in memory of your system. Aster on the other hand focuses on big data analytics and discovery. We provide a powerful platform that leverages MPP processing and 3 powerful analytic engines that combines path, pattern, graph, text, sentiment, and machine learning methods within a single platform. Aster can scale SAS models through our partnership and the SAS Scoring Accelerator.|
|Chinese Text Segmentation? So the text analysis can analyze other languages?||Yes, Aster text analytics can be extended to support any language. Our data scientist in China extended Aster to provide Chinese Text Segmentation using the APIs in the SQL-MapReduce engine.|
|DO you have a trial version of this? We want to experience it.||Yes, there is a VM image called Aster Express available on the Aster Community. Please visit and join @ aster-community.teradata.com|
|I did not hear how Aster helped cleanse data - i.e. get rid of duplicates, bad data, missing data, etc. which I understood was the real time consumming part of data preparation. Did I miss something? Seemed like most of your discussion was on integrationb||There are built in analytics using SQL-MR functions for identity matching, etc. We can also leverage machine learning methods which we have done successfully with predicting ICD9 codes. This would satisfy missing data. Duplicates we can get rid of using ANSI SQL very easily.|
|Can it take data from social media websites like facebook, twitter, blogs, instagram etc?||Data is data so regardless of its source we can work with it. Now if you mean can we take it directly from these sources, the answer is yes but it will take some preprocessing through customer sql-mr. I would advise buying an API to the data instead.|
|Are there any skillsets (programming/datascience) which are required as prerequisite before we learn Aster||Aster lowers the barriers to analytics by providing SQL calls to access complex analytics. Skills sets required to use Aster is SQL programming, exposure to analytic techniques and curiosity. Of course to use the apps deployed in AppCenter is even easier... just need to be able to navigate through a web interface and understand your business.|
|In big organizations, the business has restricted ability to create tables. Can you work just as fast in a locked down environment like this?||Yes, if you consider the Aster discovery platform as the business users data lab or sandbox, data can be accessed from the system of record, into Aster for analysis. The analyst will need to be able to create and write into tables within the Aster discovery platform.|
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