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How to automate the ideal data infrastructure

Simon Spring

Simon Spring

Account Director EMEA at WhereScape
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Data has been called a lot of things. It’s been dubbed the new oil, the new gold, even the lifeblood of modern businesses and organisations. But if people want to get the maximum benefit from data as the new gold, they can’t avoid using data warehouses and data lakes.

The first thing to stress is that they are not mutually exclusive. They serve different purposes and can often be complementary. After all, you can pan for gold in a stream and you can dig it out of a mountain.

Data warehouses are a proven means to store and manage already structured large amounts of data. Data lakes are the catch basin for all data, regardless of its relevance, structure and purpose. But while they give companies the potential to access all the data they have ever generated at any time, the amount of data is so large and diverse it requires big data techniques to manage it.

To some degree, data lakes and data warehouses are just two sides of the same coin. They both serve as storage locations for large amounts of data that are queried for analysis purposes. But each technology has a different structure, supports different formats and has been optimised for different purposes. No wonder many users think they have to choose one approach or the other. But if they take that path, they could miss out on the great opportunity provided by using both technologies.

At a time of exponential growth in data, new and innovative data infrastructures are more necessary than ever. Organisations that share data from conventional and big data sources can unearth new insights that provide an even deeper understanding of the gigantic potential of the information they store. They can also design more efficient data ecosystems by automating time-consuming, repeatable processes using new software tools.

Different sources for different data types 

Big data is already being used successfully by businesses today. Forward-thinking companies deploy big data for analytics purposes to get a more precise understanding of their customers. Their objective is to glean information from the entire pool of data that encompasses all the actions existing and potential customers can undertake.  

Data lakes make it possible to store very large amounts of data with minimal resources by taking data from different sources and storing it in its original format without processing it. But with a data lake, the data is only as useful as your company’s ability to assimilate the findings into a structured environment and the data is only processed and converted into a structured form when it is used.

For a long time, data warehouses were just gigantic databases for storing and organising data from different sources, brought together using an elaborate ETL (extract, transform, load) process and put into the required schema and format. During the analysis process, the data was usually offloaded to another platform by means of cumbersome batch loading, with the scripts often extracted manually.

The addition of ELT (extract, load, transform) capabilities made it possible to extract data from a variety of sources and load it directly into the data warehouse, before any transformation occurred. All necessary transformations were handled inside the data warehouse.

Today data warehouses support and drive business processes. Companies can spin up prototype designs in minutes and get the infrastructure up and running in days. Cloud platforms like Snowflake and Microsoft Azure Synapse can run queries in seconds and companies only pay for the amount of compute and processing power they need. Even the choice of database is no longer a 10-year decision because migration is much easier using metadata-driven tools.

If companies want to exploit the full potential of their rapidly growing volume of data, they should look to a joint, combined application of data lake and data warehouse. That path provides the most agile framework for getting rapid insight from conventional and big data ingestion now and in the future. But how do you add structure to this rich and complex data fabric?

Organisations could assign these tasks to a large team of data experts. But that is expensive and cumbersome. It is far more efficient and cost-effective to automate any time-consuming, repeatable processes and move them to an orchestration layer using automation technology. This gives IT teams full control over their applications without having to continually perform simple tasks manually.

The benefits of automation

Automation software can build a simplified model of an existing data ecosystem, allowing users to quickly, easily and cost-effectively generate their own complex and powerful data warehouse. They can use it to develop prototypes based on real enterprise data. Once the requirements are approved, the software converts the model into code. The software can achieve in a few seconds what it would have taken a developer several weeks to do in the past. This enables a team to create its infrastructure within a few weeks, instead of many months.

Recording all processes and operations performed by the automation software in metadata and storing it in a shared repository ensures users have a complete record at all times. Automation software can create complete documentation at the push of a button, with a complete history and lineage.

With flexible modern data lake platforms, it is possible to aggregate and analyse large and even unstructured data streams from multiple sources very effectively. These platforms provide end-to-end services that reduce the time, effort and cost of running data pipelines, streaming analytics and machine learning workloads in any cloud. Analytics platforms, which are also used in machine learning and for the analysis of AI data, can be set up very quickly. This is an ideal entry point, especially for startups or companies that want the benefits of big data analysis but do not have a large team available.

Organisations seeking to store and analyse large volumes of data do not have to make an either/or decision between data lakes and data warehouses. Far from it. By aiming for a symbiosis of data lake and data warehouse, they can gain more value and insight from the increasingly large quantities of data they generate and store on a daily basis.

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