Category: Big Data Integration

Category: Big Data Integration
Three Reasons to Consider Zaloni’s Data Lake Platform

Information-rich Data Landscape Today’s complex data landscape precludes reliance on a traditional, structured approach to data analysis for organizations seeking to extract the most insightful information from the data. The structured approach, characterized by pre-defined KPI’s and measures, was sufficient in the past when analysis was restricted to clearly understood enterprise data. Now, information-rich external … Continue reading “Three Reasons to Consider Zaloni’s Data Lake Platform”

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What differentiates Informatica’s Intelligent Data Lake platform?

Three reasons why you should be impressed with Informatica’s Intelligent Data Lake platform:  Cohesiveness, Cohesiveness, and Cohesiveness! Informatica raises the bar While other leading Data Integration vendors advertise a broad portfolio of products that might include Master Data Management, Self-service Data Preparation and Big Data Integration, Informatica raises the bar by combining the power of … Continue reading “What differentiates Informatica’s Intelligent Data Lake platform?”

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Comparing Patterns of Big Data Integration

Previously, I introduced five key patterns of big data integration. Here, we review those patterns, then dive into the pros and cons of each. Five key big data integration patterns With increased reliance on Hadoop and Spark for data management, processing and analytics, data integration strategies should evolve to exploit big data platforms in support … Continue reading “Comparing Patterns of Big Data Integration”

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Denodo Platform 6.0 Accelerates Data Virtualization Initiatives

Data Virtualization is a growing segment of the Data Integration market. A key player, Denodo, has just announced Release 6.0 of its Denodo Platform by end of March. Key distinguishing features of this latest release are expected to push this vendor to the forefront of Data Virtualization implementation. These features (see below) include exploitation of … Continue reading “Denodo Platform 6.0 Accelerates Data Virtualization Initiatives”

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Data Virtualization vs. Self-service Data Preparation

Data Virtualization and Self-service Data Preparation (see definitions below) may appear to be “redundant” technologies at first glance based on their overlapping product capabilities. However, by scrutinizing their respective targeted use cases and personas, strengths and weaknesses (see Table 1 below), it becomes clear that these technologies can be complementary and thus can work in conjunction (See … Continue reading “Data Virtualization vs. Self-service Data Preparation”

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Syncsort Establishes a New Milestone in Exploiting Mainframe Data with Hadoop

Syncsort is a key player in mainframe data access and integration. The company’s DMX-h product enables ingestion of historical, mainframe data into Hadoop, thereby capitalizing on the power of Hadoop for processing and analyzing mainframe data in combination with other relevant data. Syncsort has taken a significant leap forward with its latest release of DMX-h … Continue reading “Syncsort Establishes a New Milestone in Exploiting Mainframe Data with Hadoop”

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Five Patterns of Big Data Integration

As reliance on Hadoop and Spark grows for data management, processing and analytics, data integration strategies should evolve to exploit big data platforms in support of digital business, Internet of Things (IoT) and analytics use cases. While Hadoop is used for batch data processing , Spark supports low-latency processing. Integration leaders should understand the various … Continue reading “Five Patterns of Big Data Integration”

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