Cloud Data Lakes: 4 Key Challenges and How to Overcome Them

Cloud Data Lakes: 4 Key Challenges and How to Overcome Them
Planning, building, merging or transitioning to a cloud data analytics environment can be challenging, especially when an organization has both on-premise and cloud data lakes to manage. Complicating these initiatives, organizations are continuously evolving adding and removing cloud vendors as capabilities evolve, new applications are added and existing applications’ formats change. Your challenges are not unique, so why not learn from others about how to build a cloud data lake which can be agile, cost-effective, and deliver true business value? In the webinar Cloud Data Lakes: 4 Key Challenges and How to Overcome Them (shown above), you’ll hear from experts who have worked with hundreds of high-profile organizations and have identified 4 key challenges surrounding the successful implementation of cloud data lakes. Specifically, these 4 obstacles are:

Challenge #1 – Data ingestion

Just the process of loading data into your cloud environment is a challenge. Most cloud big data storage systems are “immutable”, meaning they don’t understand how to handle incremental changes to a row of data in a table. As a result, rather than load data incrementally, many organizations constantly reload entire, very large tables into their data lake - a slow laborious process. Doing this in the cloud is even tricker. (with minimal human intervention).

Challenge #2 – There is a big delta between ad hoc and production-ready data pipelines

Some cloud initiative failures or delays can be directly attributed to a lack of planning and the lack of creating a set of applications/tools to properly promote sandbox experiments into production.  Some companies are implementing open source solutions to save money, but these tools have their flaws and they can, in the end, cost more than other non-open source solutions. Other problems ensue when organizations don’t look close enough at how these different tools integrate to solve the end to end challenge of operationalizing your data pipelines.

Challenge #3 – Data and data pipeline portability

The concept of one’s data being relegated to either an on-premise or cloud environment is obsolete. Data needs to transcend the cloud vendor or cloud/on-prem choices. With the speed of change, companies must be ready to switch or incorporate more than one cloud vendor, be adaptive to new applications, new formats and changing organizational needs. Creating data portability from the start enables data teams to manage the constant level of data evolution in formats, quantity, its use and where it resides.

Challenge #4 – Managing cross-cloud and hybrid environments

Because companies will have multi and hybrid cloud environments, they have to be able to build and manage data workflows that can be orchestrated across all their cloud while dealing with the differences in security, metadata, privileges, etc. Learn the details behind each of these 4 challenges and methods for overcoming them by watching this 45 minute recorded webinar. Take the time to learn how to plan, create, merge, transition or enlarge your cloud data lake without the pitfalls many of your predecessors have endured. Just click the video above to watch now. For more information, see: