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):
Developing an Information Management Strategy Within the EMA Hybrid Data Ecosystem
Topics:
Data management
Apache Hadoop
HBase
SQL
Big data
Business Intelligence
Hortonworks
MongoDB
NoSQL
ODBC
Cloudera
John Myers
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Allgress Insight Risk Management Suite Brings Flexibility and Functionality to IT-GRC
When I started out in security, only very large organizations with a mature set of business processes dared to talk about implementing some form of governance, risk, and compliance (GRC) or enterprise program (e-GRC). They generally did it in an attempt to get ISO or similar certification, or to “move their programs to the next level,” and some, I think, attempted it just to prove they did it. Many of those efforts were monumental, costing millions of dollars and taking years to complete. However, a significant number seemed to end in compromise, yielding a smaller end result or totally failing after thousands of man hours and millions of dollars for software, systems, and consulting had been spent.
Topics:
Apps
Data management
Governance
IT Management
Software as a service
and Compliance (GRC)
Application software
David Monahan
Risk
Risk management
Scott Crawford
security
Security
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Why Data Analytics as a Service?
Waaay back in the day (say 2002), organizations would ask themselves:
Data Management: In-Memory Could Be the Mother of All Wisdom
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.
Topics:
Data management
Big data
Business Intelligence
Computer data storage
Hardware
In-memory database
Database management system
John Myers
Solid-state drive
SSD
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Data Management in 2013: Warning of the Big Data Wolf
When Aesop created the fable about the shepherd boy who cried wolf, the message was clear:
Topics:
Data management
Big data
Business Intelligence
Facebook
Harvard Business Review
Data governance
Data model
John Myers
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