A 2020 TDWI survey shows that the average data-driven organization will have more data on cloud than on premises by 2023. Many organizations have already passed this tipping point, and they will soon have only a small percentage of their data on premises.
Organizations are aggressively moving data and its management to the cloud for analytics as well as data science and machine learning because the cloud’s benefits apply directly to data management, namely elasticity for high-performance analytics processing and big data scale, but with minimal administration and entry costs for cloud platforms and tools. These benefits contribute to the success of business-driven programs in analytics, data science, reporting, warehousing, and time-sensitive operations.
However, achieving these business and technology goals depends on creating a practical, sustainable, and optimized architecture for cloud data and its applications across BI, data science, and machine learning. There are many layers in a modern cloud data architecture, but two layers stand out because they determine success or failure: the cloud data platform and cloud data integration. This webinar will drill into how these two layers work together to create a successful modern cloud data architecture.
By watching this webinar you will learn:
- What a modern cloud data architecture is and how it provides ready-for-query data for diverse and demanding advanced analytics
- Why putting most data in a single cloud data architecture enables and simplifies data integration, quality, low latency, compliance, data curation, and trust
- How modern no-code data integration contributes to the creation and long-term success of a modern cloud data architecture
- When to reassess and improve data architectures, with a focus on cloud platforms and tools