Basically, a data lake is a repository of information that are usually confused with data warehouses. However, both of them are dealing entirely with different businesses and serve different fields apart from having different architectures. If all things are considered, it won’t come as a surprise to one that cloud data lakes are a vital component of a modern data management strategy simply because of the fact that the proliferation of social data, Internet of Things (IoT) machine data, and transactional data are things that keep accelerating from time to time. It has a vital ability to store, transform, and analyze any data type and simply paves the way for new business opportunities and digital transformation. This is one of the many roles SAP DATA LAKE plays.
Data lake definition
AnSAP data lake is a central data repository that helps with solving issues related to data silo. It is most important to note that vast amounts of raw data in its native or original format is stored in data lakes. The said format can then be structured, unstructured, or semi-structured. Data lakes, are cost effective, can be scaled easily, and can be used with applied machine learning analytics, especially the ones that are in the cloud.
One more thing to note and consider is that data warehouses and lakes often complement each other. one evolving concept is the data lakehouse, which provides data management capabilities to the traditional SAP data lake. So, basically it is the combination of a data lake and a data warehouse.
Apart from the type of data and the differences in the process that one must have noted, there are some details comparing a data lake with a data warehouse solution and the same are as follows.
Essential elements of a data lake solution
- Movement of data: One can import any data type from multiple sources in its native format using Data lakes. This way businesses are allowed to scale to data size on an as-needed basis without there being a need to define data structures, schema, and transformations, and the same results in saving a lot of costs.
- Secure storage of data: Data Lakes allow storage of data in a structured, semi-structured, and unstructured format from a variety of sources like business data from CRM or ERP software, IoT devices, social media. One can even store historical data from legacy systems without any issues. Not only this, data lakes allow us to capture batch and streaming data while applying governance, security, and control.
- Analytics and machine learning: With the use of data lakes, role-based access to the information so that analytics can be run and machine learning analysis is done is allowed, without there being a need to move data to a separate analytics database. One can combine historical data with real-time data to refine machine learning or predictive analytics models to provide better and/or new results with the use of data lakes.
The various types of data lakes
There is wide variety of data lakes and data lakes can sit on premises, in the cloud, a hybrid of both, and across multiple cloud formats like the Amazon Web Services (AWS), Microsoft Azure, or Google Cloud among others.
One of the most popular type of data lake is the cloud data lake. It is a data lake that provides all the data lake features necessary, but in a fully managed cloud service.
- On-premise data lake: An on-premise data lake allows in-house IT engineering resources for managing the hardware, software, and processes. This is an approach that allows a higher capital expenditure commitment, and not only this, the data tends to be siloed.
- Cloud data lake: this is one of the most popular amongst all. In a cloud data lake, the on-premise infrastructure is outsourced and allows a higher operational expenditure commitment, but with this deployment approach,the businesses are allowed to scale more easily, apart from many other benefits.
- Hybrid data lake: In several cases, one can find companies that choose to maintain both on-premise and cloud data lakes sitting alongside each other at the same time. This is a situation that one might see mostly during migration scenarios from on-premise to the cloud and occurs very rarely.
- Multi-cloud data lake: A multi-cloud data lake is the one where two or more cloud offerings are combined with each other. For example, a business may choose to opt for AWS and Azure for managing and maintaining cloud data lakes. This is a process that requires expertise so as to make sure that these two different platforms communicate with one another effectively.
Benefits of a cloud data lake
Wondering why one should opt for a data lake? Well, if you wish to turn your data into a high-value business asset that drives digital transformation, then this is what you need. A cloud data lake allows a company to apply analytics to historical data. Not only this, new data sources, such as log files, clickstreams, social media, Internet-connected devices, and more, are allowed actionable insights.
- Cost efficient: Cloud storage allows a wide range of storage and pricing options that are highly cost efficient.
- Automatic scaling: Scaling functionality to allow businesses to compute and tap into storage capacity on demand is something that cloud storage provides.
- High Data security: There is a guarantee of high security of data with Cloud storage.
- New insights and better business outcomes with improved analytics: data can be combined in new way with a cloud data lake. There isalso an option to improve operational efficiency through the analysis of IoT data.
