Building Your Cloud Lakehouse – Do You Have a Solid Data Management Foundation?

What do you think about the latest hot topic – cloud lakehouses? A lakehouse conjures up images in my mind of peace and tranquility – a beautiful house next to a stunning lake. In the world of technology, cloud lakehouses hold a similar promise of utopia. However, without a solid foundation of cloud-native data management, your utopia can turn into a failure with unstable, untrustworthy, dirty data.

What is a Cloud Lakehouse?

A cloud lakehouse is a new way of thinking about data in the cloud that encompasses the best elements of data lakes and data warehouses. Cloud lakehouses have various curated zones that enable data to move easily from the lake to the warehouse and make trusted data available to more users.

Although cloud lakehouses are new, data warehouses and data lakes have been around for years. Data warehouses are designed to store, update and retrieve highly structured and curated data primarily for business analytics and decision-making. Data lakes, on the other hand, are designed to store massive amounts of data — whether structured or unstructured – at a much lower cost. They are primarily used for exploratory analytics and data science.

But a cloud lakehouse still possesses all of the same challenges as its older siblings – it needs enterprise-scale data integration, data quality and metadata management to deliver on its promise.

Why is it so Hard to Get Value from Cloud Data Warehouses and Lakes?

Today, increasing numbers of companies are building their new data warehouses or data lakes in the cloud. Or they’re consolidating and modernizing their on-premises data warehouses or data lakes to run in the cloud.

The problem is, many organizations struggle to see the first time to value and ROI from their cloud data warehouse and data lake.

Why? The data. According to a survey by TDWI, most organizations point to the lack of sufficient data integration, data quality and metadata management as the chief barriers to succeeding with their cloud data warehouses and data lakes.

It’s déjà vu. These are the same problems that we’ve seen (and solved) in the on-premises data warehousing and data lake world for decades. How can we avoid repeating the mistakes of the past in the cloud, and fighting these same battles yet again?

Three Common Data Management Mistakes

First, we need to take a step back. Why are organizations failing to maximize value from cloud analytics?  Three reasons in particular stick out.

  • Using manual hand coding to address data integration, data quality and metadata management issues: Hand coding is complicated, insufficient and, while ok for prototyping, does not meet the enterprise requirements of scale and maintainability within data management best practices. And you can’t reuse the code as the underlying technology stack changes. If you change or upgrade the technology, platform, or processing engine, you have to reengineer and recode it all over again. This is costly and time-consuming and hampers your ability to innovate swiftly, increasing project risk for long term success.
  • Depending on disjointed point products to achieve end-to-end data management: Using multiple, non-integrated products increases complexity as well as cost. It can take up to 10 separate products to achieve the end-to-end data management you need. And stitching together disjointed products means that you are embroiled in constant DIY mode as you deal with changing roadmaps, cost and time overruns, and – most significantly – inconsistent data governance and quality.  It’s a systems integration nightmare of different vendor offerings.
  • Relying on limited solutions from cloud vendors that only offer basic data integration or ingestion: Although offerings from platform-as-a-service (PaaS) or infrastructure-as-a-service (IaaS) vendors are designed for the cloud, they tend to have both of the above downsides. They typically offer basic data integration and ingestion, are reliant on hand-coded development, and provide capabilities that extend only as far as their own platforms.  Cloud data management requirements for modern enterprises must extend beyond any single PaaS to a multi-cloud strategy and deployment model.

What’s needed: A best-of-breed, independent cloud lakehouse data management solution that solves all these problems, and more.

Informatica Cloud Lakehouse Data Management Solution

Informatica Cloud Lakehouse Data Management is the industry’s only enterprise-class, cloud-native, end-to-end data management solution for lakehouses – as well as data warehouses and data lakes – in the cloud.

Built on the industry-leading Informatica Intelligent Cloud Services (IICS), the industry’s most advanced enterprise iPaaS (Integration Platform as a Service), the Informatica Cloud Lakehouse Data Management Solution combines best-of-breed data integration, data quality, and metadata management.

The cloud-native solution is completely automated and has advanced metadata-driven artificial intelligence (AI) capabilities. It addresses the many complex data management challenges facing businesses today. With it, you can:

  • Eliminate the risks of using hand coding and limited point solutions for data management
  • Ensure that data is cleansed, standardized, trusted, and secure
  • Enable intelligent, automated, end-to-end visibility and data lineage across your environment
  • Quickly and efficiently build data pipelines to feed your cloud data warehouse and data lake
  • Achieve all the benefits that as-a-service cloud solutions offer: scale, agility, minimal install and setup, automatic upgrades, high availability, and advanced security
  • Get faster ROI by accelerating your efforts to migrate data lakes and data warehouses to the cloud
  • Future proof your data analytics initiatives from the ever-changing technology within the underlying analytics stack (remember on-premises warehouses, Hadoop, big data, Spark, and the shift to cloud?)

With Informatica’s cloud lakehouse data management solution, you can finally unleash the power of your cloud data warehouse, data lake, or lakehouse—even across disparate multi-cloud, hybrid environments. Now you can enjoy utopia with a solid cloud lakehouse data management foundation that enables you to successfully deliver on your top priority business transformations.

Next Steps

To hear more, tune in to the Data for AI & Analytics Summit in North America or EMEA, featuring Databricks, Ventana Research, Sunrun, Prologis, Microsoft, Accenture and more. Also, learn more with our Executive Brief: Intelligent Cloud Lakehouse Data Management for Cloud Analytics.