As the race to deliver the UAW heats up, EMA sees the following vendors working toward a convergence of the data warehouse and data lake: Ahana, Amazon, Cloudera, Databricks, Dremio, Google, HPE Ezmeral, Incorta, isima.io, Oracle, SAP, Starburst, Teradata, and Vertica. EMA also anticipates that vendors that successfully deliver a unified analytics warehouse will quickly eclipse data warehouse and data lake vendors, making them obsolete, except for targeted use cases and analytical projects.
Enterprise Management Associates (EMA) has recognized that big data implementers and consumers rely on a variety of platforms to meet their big data requirements. These platforms include new data management technologies such as Hadoop, MongoDB, and Cassandra, but the collection also includes traditional SQL-based data management technologies supporting data warehouses and data marts; operational support systems such as customer relationship management (CRM) and enterprise resource planning (ERP); and cloud-based platforms. EMA refers to this collection of platforms as the Hybrid Data Ecosystem (HDE):
In skiing, the “black diamond” run or ski slope is often referred to as “high risk/high reward.” You receive lots of “reward” skiing the black diamond slopes, but you have a significant amount of “risk” associated with variable terrain, such as the presence of trees and the possibility of injury. However, the black diamond slopes are very fun to experience, and you can mitigate the risks with preparation, practice, and a really good skiing helmet.
What does Big Data mean to traditional enterprise IT? Organizations of any size and industry are becoming more and more aware of the incredible importance of capturing, managing and analyzing the data available to them. The more comprehensively companies are able to tap structured and unstructured data sources, the quicker they can refresh this data and the more successfully they make this body of data available to all business units, the better they can develop advantages in the market place. Today’s business units are demanding the rapid implementation of these big data use cases, as well as optimal resiliency, cost efficiency, security and performance.
Breaking News: Dell Acquires Enstratius to Further Complete Its Cloud Story
With its roots in mainframe job scheduling, workload automation is often seen as a relic in today's age of cloud, Big Data, mobile management and DevOps. Do we even still need workload automation as a separate discipline or should we simply roll the management of batch jobs into other automation disciplines, such as IT process automation? Is the market for workload automation software stagnating or is there still potential for growth?
As I review my series of #100linesOnBIDW blogs over the last couple of weeks, I found myself looking at the Data Management posting. I covered when to apply schemas, Big Data, and data governance. What I left out was technical implementation concepts for data management systems like row vs. column orientation; in-memory vs. spinning disk primary storage; and symmetric multiprocessing (SMP) vs. massively parallel processing (MPP). Processing and storage were the “developments” of 2012. I left 2013 for the “how to use” Data Management platforms.
When Aesop created the fable about the shepherd boy who cried wolf, the message was clear:
“Back in the day”, Pablo Picaso once said: