Analytics Capabilities of AWS, Azure and Google Cloud

Enterprise analytics and business intelligence (BI) systems offer amazing capabilities for users to see “big picture” headline data or transaction-by-transaction micro data, or aggregates anywhere between. This ability to easily adjust the organizational focus from totals to transactions makes BI systems ideal for auditing, compliance and project development. They’re also excellent at financial modeling — including budgeting, forecasting and cash flow management.

Analytical engines are the eyes of Artificial Intelligence (AI), recording transactions and shifting focus from macro to microanalysis. They require a big brain to work right. The big brain is the huge data warehouse beneath the analytics system. Such warehouses often involve both relational and non-relational databases and structured and unstructured data. The data is organized with enterprise software such as Oracle Business Intelligence Enterprise Edition (OBIEE), formerly know as Hyperion.

OBIEE and similar enterprise software is now available in cloud versions which can be used with any of the major Platform-as-a-Service (PaaS) providers including Amazon Web Services (AWS), Google Cloud and Microsoft Azure. A new white paper from Datavail, Comparing Database Services Within the Leading Public Clouds, compares the capabilities of these three services on a variety of metrics, including computational power, storage options, networking, and pricing.

The white paper also looks at the business analytics offerings. Below are some of the findings.

Big Data Analytics

  • Google Cloud’s Analytics Engine contains a variety of components for Big Data analytics. MapReduce is used to process and generate large data sets. BigQuery is for running analytics on very large data sets or warehouses. Cloud Dataflow is for real-time data analysis.
  • At Amazon, Elastic MapReduce is used to organize big data sets. Amazon’s cloud is friendly to a variety of analytics software. “Amazon EMR facilitates running third-party frameworks such as HBase, Apache Spark, Flink, and Presto” (from Datavail white paper).
  • Microsoft offers HDInsight as an analytics engine for Azure. HDInsight is basically Hadoop on Azure. The latest version of HDInsight includes Spark 2.0. HDInsight supports a variety of Apache offerings, including Hive, Tez, Zeppelin, and HBase.

Data Visualization

  • Google offers DataLab as its visualization engine. Amazon has QuickSight for combining structured and unstructured data sets for analysis. Azure offers PowerBI in three “national centers” in the cloud.

Machine Learning Systems

  • Google Cloud Machine Learning has APIs for Cloud Speech, Cloud Vision, Google Translate, and Cloud Natural Language. It also offers an integrated messaging system called Pub/Sub.
  • Amazon Machine Learning is cloud-based data analytics on AWS. It analyzes data from a wide variety of input databases, looks for patterns, and then outputs predictions. It provides low-latency, high-throughput metrics that allow for the rapid processing of Big Data inputs to generate recommendations in real-time.
  • Azure Machine Learning “uses best-in-class algorithms and a simple drag-and-drop interface to go from idea to deployment in a matter of clicks,” according to Microsoft. They also claim that Office 365 offers process documentation capabilities and that Dynamics 365, its cloud-based CRM program, is useful in creating workflows.

Conclusion

There are many advantages to using could platform providers for your big data needs — beginning with significant cost savings over legacy systems. For a full discussion of these issues, download the Datavail white paper, Comparing Database Services Within the Leading Public Clouds. The number of options and possible configurations makes it difficult to connect the services you need and get a straight answer on what it will cost. Datavail can assist you with the calculations, software license negotiations and possible configurations for your cloud database systems.

Big data analytics systems require maintenance, monitoring and tuning. When properly configured and maintained, they can get generate the kind of real-time data analysis that allows enterprises to put just the right message on the screen — whether it’s in a clinic, a trading room, a traffic signal management center, or anywhere big data analysis is needed instantly. Contact Datavail today to learn more about what BI can do for you.

Datavail is a specialized IT services company focused on Data Management with solutions in BI/DW, analytics, database administration, custom application development, and enterprise applications. We provide both professional and managed services delivered via our global delivery model, focused on Microsoft, Oracle and other leading technologies.

 

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Eric Russo
Senior Vice President of Database Services
Eric Russo is SVP of Database Services overseeing all of Datavail’s database practices including project and managed services for MS SQL, Oracle, Oracle EBS, MySQL, MongoDB, SharePoint and DB2. He is also the Product Owner for Datavail Delta, a database monitoring tool. He has 21 years’ experience in technology including 16 years in database management. His management success and style has attracted top DBAs from around the world to create one of the most talented and largest SQL Server teams. He has been with Datavail since 2008: previous to that his work experiences include DBA Manager at StrataVia, Senior Web Developer at Manifest Information Systems and SQL Server DBA at Clark County, Nevada.

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