Data lake storage platforms are all the rage right now. And with good reason - for organizations with business problems and use cases that require a repository which can quickly onboard and ingest datasets of myriad scales and levels of refinement, a data lake can take the pain away from management of big data.
However, data lakes are harder to implement and run than one might initially believe. Without comprehensive best practice policies, trained staff, and management tools in place, a data lake can easily turn into a data swamp. While companies are hopping on the data lake bandwagon (or boat, if you will), choices such as whether to buy or build their data lake platform, and what tools they employ, can make or break the success of this new system. When done right, organizations with data lakes may outperform their peers by as much as 9% in organic revenue growth. It’s definitely worth a try.
What Is a Data Lake?
Simply put, a data lake is a centralized repository capable of storing all of your structured and unstructured data at a range of scales. An organization is able to onboard data with minimal upfront improvement, and run analytics, machine-learning, and big data processing for fast guidance towards better data-driven decisions.
Organizations that implement data lake systems save time in data processing as it mitigates the need to structure, define, and curate data, as one would for storage in a data warehouse. Companies often require the use of both a data lake and a data warehouse, as the two serve different purposes and offer different solutions.
Data Lake Management Tools
As stated above, without a thorough outline of practices, as well as a well-stocked management toolbox, the waters of a data lake can become muddy and impenetrable, making further use messy and inefficient, and defeating the purpose of implementation. Unless you’re already an expert, or have one on staff, there’s no reason not to start with a solid set of pre-built programs and tools. Discussed below are four essential implements for successful data lake operation.
Data Lake Architecture
Data lake architecture differs from the traditional business intelligence architecture in that the data sources may be more varied, and the data itself can be structured or unstructured. In the lake, all data integrates into a raw data store which ingests said data in its raw form. The processes for structured versus unstructured data differ only slightly:
Structured data enters raw data storage. From there it can enter the analytical sandbox for analysis and data science; or, it may enter the batch-processing engine. The processed data will then enter the processed data stores. Reports may then be generated.
Unstructured data may enter raw data storage, and then transfer to the batch-processing engine. Or, it may go through a real-time processing engine, which works as the new unstructured data is read. The processed data will then enter the processed data stores. Reports may then be generated.
This varied processing architecture accrues value through its ability to cater to multiple uses by both stakeholders and end-users without distillation and packaging for defined purposes.
Data Lake Catalog
This is perhaps the most important component of any big-data landscape management system. Different data have different value which varies based on the lineage, quality, and source of creation of the data. These individual pieces may be useful to some analyses and not others; determining this would be near impossible without a resource catalog.
Utilization of a data lake catalog becomes an essential organizational strategy which prevents the conception of the dreaded data swamp. Catalogs may be customized to the individual needs of the organization. It increases navigability, comprehension of business and technical metadata, and mitigates the effects of redundant datasets.
Data Lake Search Engine
Imagine the internet without search engines. At a smaller scale, this is what a data lake might feel like without an effective search engine. Information is useless if you can’t find it, rendering all that architecture and thorough cataloging null and void.A fast and efficient search engine can locate datasets, as well as individual datum inside larger files.
But a search engine will not only help you locate datasets, but also allow an organization to scale, analyze, and scrutinize data in your data lake.
Data Lake Security
Raw data storage strategies mean that data is often stored in a more readable format, compromising the security of information in the event of a break-in. The requirements of such storage solutions dictate an active and secure management policy. Governance and security should be a top priority for any company implementing a data lake, and it will need to be specific to the system in use.
At minimum, requiring user authentication, user authorization, data in motion encryption, data at rest encryption, and access for certain users to processed but not raw datasets will keep your data lake safe and secure.
Implementing a data lake system for the first time? Check out our other article, Data Lake Best Practices You Need to Follow, for tips that will help you skirt the rookie mistakes and build a repository you can be proud of.
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