Start your data-driven journey  with a solid foundation. While a quick-fix data stack might seem sufficient initially, a well-crafted architecture can fuel exponential growth. Dive deep into the world of BI and data science, uncover hidden gems within your data, and embrace the power of cloud data warehousing. Your choice today could be the catalyst for tomorrow’s innovation. 

Don’t let the chains of proprietary data formats and vendor lock-in hinder your company’s growth. As your team expands and needs evolve, incompatible BI and data science tools can create a tangled web of complexity. And with the potential for acquisitions, merging datasets from different warehouses can become a daunting task. It’s time to break free from these limitations and explore more flexible data solutions. 

Imagine a world where your analytics tools have a front-row seat to every customer interaction, every operational detail, and every business metric. A solution that scales with your growth, doesn’t break the bank, and empowers you to adapt to changing needs without being locked in. That’s the power of a truly flexible data platform. 

Considering your limited resources and changing priorities, how can you achieve this? This blog explains why an analytics stack based on distributed query engines, open file, and table formats, and the contemporary data lake is the best architectural choice for corporations. 

Pick a Data Lake

By using a data lake, you can affordably store an infinite amount of structured or unstructured data in a single central repository. In the past, people viewed data lakes negatively because they couldn’t apply advanced analytics on top of them, and they became swamps. 

Now you can take advantage of both the cost savings of a cloud data lake and the quick analytics of a traditional warehouse by adding data-warehouse-like analytics capabilities to your data lake. You can achieve the best of all worlds. 

Choose a Format for Open Files

After selecting a data lake provider, you must choose the data storage strategy.  

Despite their peculiarities, developers designed the open file formats to avoid vendor lock-in, and you practically own your data. 

On the other hand, when you stream data into a conventional or cloud data warehouse, the system transforms it into a proprietary format, making it more difficult for you to move or migrate the data to a different provider in the future. This strategy works well for the warehouse, but customers may not find it as appealing. 

To maintain your flexibility and choices in the future in case a rival supplier offers more advantageous terms, services, or benefits that align with your company’s needs, selecting the format for an open file is an important step. 

Choose the Appropriate Table Format

To arrange the data, you must choose a table format after choosing an open file format. Each table has advantages, thus picking the appropriate table format is equally as crucial as choosing the best file format.  

Accept open-source technology

Open-source technologies offer many benefits, including affordability, community-driven innovation, and adaptability. 

Companies with minimal resources benefit from the increased flexibility, customization possibilities, and transparency that these solutions provide. Corporates can create a flexible, affordable, and robust data infrastructure that enables them to gain valuable insights and expand their businesses. 

Select an Analytics Engine

The next step is to choose an analytics engine that enables you to query this prepared data and discover patterns, insights, and other information. Your analytics engine should ideally be extremely effective and performant, but it should also be: 

Scalable: If you add more datasets to the mix or expand your data lake, the engine should operate efficiently. 

Flexible: Maintaining optionality is essential; you shouldn’t build your analytics stack on a query engine that restricts you to a specific cloud, and it should remain compatible with a wide range of BI and data science tools. 

Future-proof: When you employ more BI and Data Science tools or add new data sources (like a recently acquired firm with its warehouse), the engine should be flexible enough to adjust over time. 

When you employ more BI and Data Science tools or add new data sources, it should maintain enough flexibility to adjust over time. 

How to Fully Utilize your Data using a Contemporary Data Lake?

The development of historical systems into a modern data lake addresses the issues of cost and speed of data accessibility. The data architecture is altered by contemporary data lake platforms, greatly expanding the data’s potential. 

Commodity storage and computing infrastructure provide the foundation of a modern data lake. This guarantees that as their demands change, entrepreneurs may easily and affordably scale up or down their resources. 

It also depends on open file and table formats, which ensures data ownership and portability. Startups can avoid vendor lock-in and keep control of their important data by utilizing open formats.  

Naturally, searching the massive volumes of data stored in the lake requires a high-performance and scalable query engine. 

Organizations can fully utilize their data by adopting these developments in contemporary data lake design, which will guarantee enhanced performance, governance, and accessibility for data-driven decision-making.  

Conclusion

Organizations can overcome issues with data access, optimize query performance, strengthen data governance, and streamline data consumption for business intelligence by adopting a contemporary data lake architecture. As you set out on your data-driven path, we strongly advise you to investigate an affordable analytics engine that has shown to be perfect for expanding businesses. 

Read Whitepaper Data Lake Navigation: Unraveling Insights in the Sea of Information