Data is considered the new gold. If you want to get the maximum benefit from them, you cannot avoid data warehouses & data lakes these days. While data warehouses are a proven way to store and manage already structured large amounts of data, data lakes are the collecting tank for all data regardless of its relevance, structure, and purpose.
Data lakes and data warehouses are just two sides of the same coin. Both serve as a storage location for storing large amounts of data queried for analysis purposes. However, each technology has a different structure, supports other formats, and is optimised for various purposes. However, many users feel that they have to choose one approach when operating data lakes and warehouses. Their great opportunity lies precisely in the joint application.
In times of exponential data growth, new, innovative data infrastructures are more necessary than ever. The everyday use of conventional and big data enables new insights that lead to an even deeper understanding of the tremendous data potential. Time-consuming, repeatable processes can also be automated using new software tools so that data ecosystems can be designed much more efficiently than before.
Deep Diving In The Data Lake
Large amounts of data are the engine of successful companies today. Forward-thinking companies collect Big Data for analytics to gain a more accurate understanding of their customers. The goal is to get information not just about a single piece but about the entire pool of data puzzle pieces, encompassing all actions performed by existing and potential customers.
The data is stored in the data lakes, i.e., storage areas that can absorb data from different sources and save it in its original format without processing it immediately. In this way, substantial amounts of data can be stored with minimal resources. Unlike a data warehouse, which processes all incoming data directly using Extract-Transform-and-Load ( ETL ) or Extract-Load-Transform (ELT), the data is only processed when used.
Structured Data Collection In The Data Warehouse
At the latest, when the masses of data stored in the data lakes are used, they must be converted into a structured form. In the data warehouse, there are predefined formats and fields for this, such as telephone numbers, transaction prices, or time stamps.
For a long time, data warehouses were nothing more than a gigantic database for storing and organising data from different sources, which was brought together with a complex ETL process and into the required schema and format. The data was usually outsourced to another platform using cumbersome batch loading in the analysis processes, with the scripts often being extracted manually.
In the meantime, data warehouses have long since matured from players to playmakers. Instead of simply storing data, they support business processes and significantly control them. Because thanks to efficient, new technologies, it is now possible to design prototypes within minutes and have the infrastructure ready for use within days. With cloud platforms such as Snowflake or Microsoft Azure Synapse, queries can be executed in seconds. Only the amount of computing and processing power required is billed. The choice of database is also no longer a ten-year decision since the migration has been significantly simplified thanks to metadata-driven tools.
The Future Belongs To Agile Infrastructures
Companies and users who want to use the full potential of the rapidly growing amount of data will not be able to avoid a joint, combined application of data lake and data warehouse. But to obtain sustainable insights based on conventional and big data, the infrastructure must also be adapted to the new requirements. Rigid and inflexible was yesterday. The infrastructure of the future will be agile. It should continuously adapt to new conditions and develop data sources regularly.
Of course, it would be possible to assign these tasks to a large team of expensive data assistants. However, it is more efficient and cost-effective to automate all time-consuming, repeatable processes and use automation technology to move them to an orchestration layer where IT teams can take complete control of their applications without manually performing simple tasks as they used to.
Data Warehouse Automation
Automation software, such as WhereScape, creates a simplified model of an existing data ecosystem, based on which users can quickly, easily, and inexpensively generate their complex and robust data warehouse. They create their graphical user interface using drag-and-drop and develop prototypes based on accurate company data. Once the requirements are approved, the software converts the model into code and physically represents it. For a programmer, that would probably have meant several weeks of work. The software only needs a few seconds for this. In this way, a team can now create its infrastructure within a few days, a job that would have taken several months in the past.
To ensure that users have a complete overview, all processes and procedures that the automation software executes should be recorded in metadata and stored in a repository. Automation software creates full documentation at the touch of a button, with complete history and additional track-back and track-forward functions.
Data Lake Automation
Flexible data lake platforms like Qubole can effectively aggregate and analyse large and unstructured data streams from different sources. They provide end-to-end services that reduce the time, effort, and cost of running data pipelines, streaming analytics, and machine learning workloads on any cloud. On these platforms, the tasks that arise in day-to-day business are carried out automatically so that there is almost no administrative effort. Platforms like Qubole can be connected to different clouds. In machine learning and data analysis, analysis platforms can be used very quickly. This is an ideal way to get started, especially for start-ups or companies that want to get into extensive data analysis but don’t have a large team at their disposal.
Anyone who wants to store and analyse large amounts of data today no longer has to make an either/or decision, as was the case just a few years ago. A data lake should be viewed as a complement to the data warehouse. The big data environment also collects seemingly unimportant data whose connection to the information currently being used is not immediately apparent. It’s also possible to see data warehouses as a standalone source for data lakes that, like any other analysis results, provide new, valuable insights when combined with other data.
ALSO READ: This is the difference between model- and data-centric MLOps