VIQTOR DAVIS Blog

If you ever wanted a house by the lake, now is the time

Store your data in the best of both worlds, a lake house


Today, an increasing number of organizations move their data initiatives into the cloud. Or they’re consolidating and modernizing their on-premises data warehouses and data lakes to run in the cloud. Unfortunately, many organizations are only satisfied to a limited extent. Many believe data quality and data management are the main barriers and they specifically mention integration and metadata as the greatest hurdles.

What is the difference between a data lake and a data warehouse?
Let's briefly list the differences. The two types of data storage are often confused but are much more different than they are alike. Data lakes and data warehouses are both widely used for storing big data, but they are not interchangeable terms. A data lake is a vast pool of raw data, the purpose for which is not yet defined. A data warehouse is a repository for structured, filtered data that has already been processed for a specific purpose. Organizations often need both. Data lakes were born out of the need to harness big data and benefit from the raw, granular structured and unstructured data for machine learning, but there is still a need to create data warehouses for analytical use by business users.

While a data lake works for one business goal, a data warehouse will be a better fit for another. Disappointing results are often the consequence of limited consultation between technology and business owners, which leads to wrong choices being made early in the process. Systems are often not suitable for the business they have to serve. Each purpose requires different sets of eyes to be properly optimized for.

The best of both worlds is called a lake house
The best Data Management Solution combines best-of-breed. It is completely automated and has advanced metadata-driven artificial intelligence capabilities. Today we have lakes and warehouses coming together in a unified architecture also known as a cloud lake house. A lake house offers you a view into not only what happened and what is happening, but also what is going to happen. Today you can solve for a number of use cases: fraud detection, risk reduction, next-best action, supply chain optimization, just to name a few. All delivered with the promise of cloud solutions: quality, agility, scalability, and lower capital costs. It addresses the many complex data management challenges facing businesses today.

A lake house is what you get if you redesign the old concept of a data warehouse with the data lake knowledge we have today. Implementing similar data structures and data management features to those in a data warehouse, directly on the kind of low-cost storage used for data lakes. In this way you get best of both worlds. Modern additional features to address data governance, auditing, retention and lineage can be added easily, as well as tools that enable data discovery and data usage. All from a single system.

Key features of a lake house are:

  • Support for multiple types of data transactions
  • Robust governance and auditing mechanisms
  • Activating direct BI support on the source data
  • Easy scaling in users and data sizes
  • Open for any other system or technology to integrate
  • Support for new data types such as video, images and audio
  • Streaming data to support real-time

Merging the benefits of a data lake with those of a data warehouse in one system means that you can work faster because you do not have to enter multiple systems. For so many organizations, operational decisions are still based on structured data from traditional legacy operating systems. Today we can add streaming data to that with Machine Learning and AI. Think of speech recognition, text mining, images, video and other information from internal and external sources. In this way, many new propositions are possible. With the use of a lake house it is possible to control versioning, governance and security right away.

Mistakes to avoid if you start if you get started

  • Not having a proper plan
    If you do start without having a proper ‘data to the cloud’ plan or an advisory process, you often end up with multiple, non-integrated products that increase complexity as well as cost. It can take up to 10 separate products to achieve the end-to-end data management you need, having them all properly integrated and cooperate can be a hassle, which is at the expense of the end result.
  • Using the ‘do it yourself’ method
    Hand coding is complicated, insufficient and, while ok for prototyping, does not meet the requirements of scale and maintainability that makes a solution successful. Using manual hand coding to address integration, quality and metadata management issues must be avoided at all times.
  • Shoot for the stars with a lack of equipment
    It may seem attractive to go for the simple quick fix solution. Rely on solutions with limited capabilities that only provide basic integration will eventually work against you. Even though it is IaaS or PaaS technology, they often provide capabilities that extend only as far as their own platform, without any proper integration options.

Conclusion
The performance of a tailored data warehouse system can still be faster than lake house technology, but the costs will decrease considerably. There is still a lot to win in lake house technology, for example front-end usability, this can improve significantly to meet the high quality of current hyper cool BI tools. As lake house development continues it will bridge the gap between data science and business reporting. It is fast, cost friendly and fits many different data needs.

Learn more
Join our “migration to the cloud” webinar on Thursday November 5th 2020 and get an inside on what path you should walk and what mistakes to avoid.

Agenda:

  • Accelerated Digital Transformation
  • Investments in Cloud
  • Business drivers for Data in Cloud, accessibility , single source of truth, speed
  • Data in the Cloud – how - methodology
  • Successful migrations & Pitfalls
  • Technology point of view: Integration, ingestion, DQ, data preparation, governance, people, processes, curation
  • Transition to Cloud Data Lake house

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