This is the fifth blog in our series helping you understand all about cloud, when you are in a dilemma to choose Azure or AWS or both, if needed.
Before we jumpstart on the actual comparison chart of Azure and AWS, we would like to bring you some basics on data analytics and the current trends on the subject.
If you would rather like to have quick look at the comparison table, Click here
This blog is intended to help you strategize your data analytics initiatives so that you can make the most informed decision possible by analyzing all the data you need in real time. Furthermore, we also will help you draw comparisons between Azure and AWS, the two leaders in cloud, and their capabilities in Big Data and Analytics as published in a handout released by Microsoft.
Beyond doubts, this is an era of data. Every touch point of your business generates volumes of data and these data cannot be simply whisked away, cast aside as valuable business insights can be unearthed with a little effort. Here’s where your Data Analytics infrastructure helps.
A 2017 Planning Guide for Data and Analytics published by Gartner written by the Analyst John Hagerty states that
The Key Findings as per the report are as follows:
The last point emphasizes on how cloud is playing a prominent role when it comes to Data Analytics and if you have thoughts on who and how, Gartner in its latest magic quadrant has said that AWS and Azure are the top leaders. Now, if you are in doubt whether to go the Azure way or AWS or should it be the both, here’s the comparison table showing their respective Big Data and Analytics Capabilities
|Elastic data warehouse||A fully managed data warehouse that analyzes data using business intelligence tools.||Redshift||SQL Data Warehouse|
|Big data processing||Supports technologies that break up large data processing tasks into multiple jobs, and then combine the results to enable massive parallelism.||Elastic MapReduce (EMR)||HDInsight|
|Data orchestration||Processes and moves data between different compute and storage services, as well as on-premises data sources at specifed intervals.||Data Pipeline||Data Factory|
|Cloud-based ETL/data integration service that orchestrates and automates the movement and transformation of data from various sources.||AWS Glue Data Catalog||Data Factory + Data Catalog|
|Analytics||Storage and analysis platforms that create insights from massive quantities of data, or data that originates from many sources.||Kinesis Analytics||Stream Analytics
Data Lake Analytics
Data Lake Store
|Streaming data||Allow mass ingestion of small data inputs, typically from devices and sensors, to process and route data.||Kinesis Streams |
Event Hubs Capture
|Visualization||perform ad-hoc analysis, and develop business insights from data.||QuickSight (Preview)||Power BI|
|Allows visualization and data analysis tools to be embedded in applications.||Power BI Embedded|
|Search||A scalable search server based on Apache Lucene.||Elasticsearch Service||Marketplace—Elasticsearch|
|Delivers full-text search and related search analytics and capabilities.||CloudSearch||Search|
|Machine learning||Produces an end-to-end workfow to create, process, refne, and publish predictive models from complex data sets.||Machine Learning||Machine Learning|
|Data discovery||Provides the ability to better register, enrich, discover, understand, and consume data sources.||Data Catalog|
|A serverless interactive query service that uses standard SQL for analyzing databases.||Amazon Athena||Data Lake Analytics|
Click here to read the entire guide published by Microsoft Azure Team:
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