Blending Cloud Data Lakes and Cloud Data Warehouses for Speed, Trust, and Agility
TDWI Speaker: David Loshin, President of Knowledge Integrity
Date: Thursday, August 22, 2019
Time: 10:00 a.m. BST
The desire for modern reporting and analytics environments is inspiring strategic migration of data warehouses and data lakes to the cloud. Unfortunately, cloud architects seldom consider complementary strategies for the data lake and data warehouse, leading to inefficiencies and inconsistencies across these two data environment paradigms. Questions arise about where data sets are persisted, how and when data is loaded into data warehouses, how data sets are shared by data consumers, the processes by which streaming data is ingested and integrated, and how to ensure a level of trust once the data sets have been properly seated.
In this webinar, we suggest that cloud data lake and cloud data warehouse strategies do not need to conflict with each other. Instead, data lakes and data warehouses can be blended to speed time to reporting and analytics while raising overall trust in the data and enabling fast deployment and turnaround.
At the same time, cloud resources can be leveraged to automatically enable elastic scalability and concurrency to meet the performance demands of varying workloads among different user communities. An effective cloud data strategy will leverage the synergistic power of blending the data lake with the data warehouse to balance discovery, cleansing, and data governance with data accessibility across the two paradigms to enable both predefined reports and ad hoc analytics.
In this webinar, attendees will learn about:
- Effective ways of managing streaming data velocity
- Approaches for standardization and data preparation for ensuring data trustworthiness
- Ease of design, development, and deployment of data warehouse architectures
- Simplifying ingestion and access to data for analytics
- Speeding time to value with trusted governed data
- Synchronization and coherence
- Security and data protection
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