Data Lake Projects: An In-Depth Q&A Compilation by Lingaro
In theory, the advantages of a modern Data Lake over a legacy data warehouse are quite obvious. In practice, they are not. Many businesses’ data warehouses have been proven to work with their existing analytics workflows. The promise of improving these workflows with a new strategic approach is met with strong skepticism and detailed questions.
Fortunately, we have answers. We have years of cutting-edge Data Lake experience that includes global scale data solutions based on Microsoft’s Azure cloud platform.
In this blog series, we have compiled our answers to some of the most common questions we have encountered along the way. We hope you will find them useful, as you explore your own Data Lake opportunities.
“Is it a problem that we are building data analytics solutions as part of isolated projects?”
“Lingaro recommends an approach based on multi-layer frameworks which allow to”:
Setup the project team with ease, in many cases you do not need low level technology experts, because the framework allows for work on higher levels of abstraction;
Deliver faster – reuse low level components, in many cases the development is replaced by configuration;
Add a given functionality once and reuse it in many places;
Standardize code and architecture;
Limit code redundancy;
Reduce costs of ongoing development, you may start the next projects from a different level, reusing previous work;
Introduce a lot of features which are hard to do separately and prepare from scratch for individual cases (advanced monitoring, management, CI/CD, automated data quality validation, advanced profiling and tuning, auto documentation etc.);
And still you may keep the customization capabilities to reflect your individual needs.