How to Design a Data Lake with Business Impact in Mind
A quarter of organizations surveyed by TDWI in 2017 say they already have a data lake in production, while another quarter say their lake will be in production within 12 months. Although data lakes are still rather new, user organizations have adopted them briskly. Why has the data lake gotten so popular, so fast?
July 24, 2018
Cloud or On Premises for Analytics: Why Not Choose Hybrid?
Business users today are clamoring for more analytics so they can explore data in detail and build business-driven analytic models. However, many organizations are on the horns of a dilemma: Where should they put the data and process analytics workloads? The cloud offers tempting flexibility and price points—plus the “gravity” of cloud-based applications is pulling more data generation into the cloud, and it can make sense to do analytics processing on cloud platforms. On the other hand, on premises is where many organizations feel more comfortable and have more experience from data management, governance, and security perspectives. It’s a big decision.
July 26, 2018
Achieving High-Value Analytics with Data Virtualization
Analytics projects are critical to business success, and as a result, they are growing in size, number, complexity, and perhaps most important, in their data requirements. TDWI finds that data scientists, business analysts, and other personnel need to view and access data that resides in multiple sources, both on premises and in the cloud, to draw insights from data relationships and discover important patterns and trends.
July 31, 2018
Getting Started with Data Integration in the Cloud
Cloud continues to rise in importance as a platform for many IT systems, including those for data integration. Many organizations have now achieved a maturity level where they are using multiple cloud-based applications and online data sources. These users now need data integration tool platforms that support hybrid data environments so they can unify on-premises and cloud-based data sources and targets. Similarly, users increasingly need data integration processing to run natively on clouds (not just on premises), so that data integration functions and related capabilities are closer to software-as-a-service (SaaS) applications, Web data sources, multiple clouds, and increasingly popular cloud-based databases, data lakes, and data warehouses.
September 19, 2018