Through its ability to store huge amounts of varied information, data lakes can be used for flexible analytics that support smart decision making. Data from the various sources can be used for many different applications and analyses, including real-time analytics and machine learning. The aim is to be as agile as possible in achieving optimal results and responding to new business opportunities.
One of the biggest benefits of a data lake is the flexibility to drive your business forward through agile analytics that can measure performance and improve productivity by making informed judgements, for example. This is done by taking advantage of consistent big data and deep learning algorithms to deliver real-time decision analytics.
Although data lakes can be built on premise, they lend themselves to being created in the cloud. Cloud offers the performance and scalability that data lake requires, together with economies of scale and access to a range of analytics engines. Enterprises also benefit from enhanced user availability.
Why build a data lake?
Schema-based traditional data warehouses are not optimized for the variety and sheer volume of big data. Data lakes, on the other hand, can store data from diverse sources in their native format, including multimedia, social and XML. Data lakes are infinitely scalable, designed for rapid data ingestion and support the demands of the emerging Internet of Things (IoT), all of which make them the perfect partner for big data.
In addition, data stored in data lakes is easily accessible. Subsets can be made available to different groups within an enterprise, for example, whether it is for self-service or delivered to data scientists for analysis.
Data lakes come with the promise of more visibility into your data and breaking down silos around your enterprise by storing all data in one central repository. But a large number of data lake initiatives end up failing due to the organizational and cultural changes required to create and operate business projects on top of data lakes.
This is where Orange Business Services comes in. We help you figure out if you have the business cases necessary for building a data lake and help you through the complexities of creating it. Enterprises often forget that data lakes come with no structure, so they need to construct a catalog integrated with governance. Without this, the data has no context, and data scientists will waste time trawling through undefined data sets for data sources.
Key features of a data lake
Ability to store all native data types
Unlimited ways to query data
Elimination of data silos
Schema on-read offering unparalleled data ingestion speeds
Highly extensible at lower cost
Six steps to building a data lake in the cloud
1. The scoping phase is very important. If the data lake is to work effectively, you should always start with concrete use cases and continuously examine their business value. At the beginning, it is a good idea to choose high-quality data sources.
2. Carefully plan your data lake build and deployment outlining the skills that will be necessary to make it a success.
3. Storage costs to load data into the cloud may be relatively inexpensive. But if you want to get data back into your own systems or replicate it over availability zones, it can be very expensive. Consider if this is something you may need to do in the future.
4. Include the active and secure management of data in your data lake plan.
5. Implement data governance and a data catalog in your data lake from the beginning, so you know what data is in the data lake, why it is there and who is using it – or you will end up with an unusable data swamp.
6. Load data in stages, perform data-quality checks and manage it. Your results will only be as good as the quality of your data.