Why Do Companies Use Data Lakes?
Modern enterprise-level computing operations have to capture a truly monumental amount of information every single hour. As the scale of data has grown almost exponentially over the years, so has the scale and complexity of data stores. Traditional databases couldn’t possibly keep up with the massive numbers of records that have to be created today, which is why so many firms ended up looking for alternatives to them.
For a while, it certainly seemed as though the new breed of data warehouses would fit the bill. Enterprises that had to harvest information from all of their inputs regarding every possible function of their businesses ran to adopt this new paradigm even as the streams of data they were collecting turned into raging rivers that some might call fire hoses. Into this scattered market entered an even new concept that attempts to refactor the data processing question into a tranquil lake as opposed to that torrential downpour.
When businesses turn to this kind of digital infrastructure, they’re often looking to make sense out of this otherwise immeasurable flood.
When Data Lakes Beat Out Warehouses
Since lakes are taking over in a space currently occupied by already existing operational structures, the data lake vs data warehouse debate is currently pretty heated. Proponents of lakes say that one of the biggest reasons that companies are turning to them is the question of vendor lock-in. When an enterprise-level user stores all of their information into a conventional warehouse, they’re locked into a single vendor who processes data on their behalf. In general, their storage and analytic algorithms are bundled together into a package that’s not easy to separate into disparate parts.
Others might be dealing with a specific processing engine that requires all of their information to be formatted a certain way as its own inputs can only understand data presented in said format. Those who’re dealing with this issue might not notice it until they finally get a flow of information that’s in some format that the warehouse engine doesn’t understand. Writing custom software to convert it isn’t an easy task, especially when there’s no API to work with.
Admittedly, these issues don’t normally come up when dealing with simple and concrete data analytics pipelines. Data warehouses do a great job of making information available and they certainly help managers draw insights from data that they might not otherwise get a chance to visualize and understand. As soon as you get into things like log analytics or processing data through machine learning technology, data warehouses will struggle. They’re normally based around relational database formats, which aren’t designed to manage semi-structured information.
Instead of going through the incredibly complex process of normalizing and cleaning all of the incoming data, it makes more sense for organization-wide IS departments to transition to a data lake.
Shifting to Data Lakes the Easy Way
Change is never easy, and that’s especially true when you’re working with IT in an enterprise market. However, the promises made by data lake proponents are attractive enough that some are making the leap. They offer an affordable single repository for all of your information regardless of what you want to store, which is why so many are now taking a dip in these virtual bodies of digital water. Structured and unstructured data can fit in together, which is certainly helping to ease the transition in many cases.
IS department staffers and technologists have found that streaming data straight from a transactional database into a data lake abstraction is a simple process. Doing so gives them the freedom to run analytics on it at a later date. Best of all, semi-structured seemingly random data points like those that come from a clickstream analysis can be moved in real-time without having to write some kind of intermediary back-end script that forces it into some kind of relational format.
However, some have taken this entirely too far and that’s where most of the criticism of data lake technology seems to be coming from. Data lakes unfortunately have to be managed carefully and the information stored in them still has to be organized in some way that makes sense and is, ideally, human readable. Locating completely unstructured data later on will simply be too difficult.
True believers in the technology have developed new solutions that get around this problem without requiring engineers to write any customized code, which has helped even skeptical businesses adopt this technology.
The Rise of Open Data Lakes
Building an open data lake is key to ensuring that any kind of processing engine can read information that comes out of the lake. That’s why an increasing number of developers are turning to platforms that don’t store this information in any proprietary format. While this might seem like it could get confusing since there are a number of different competing standards vying for market dominance, it’s actually aiding adoption.
Competition, even in the open-source community, has helped to ensure that data processing software ships with a relatively low incidence of bugs. The fact that there are multiple vendors in the space has also proven helpful to IS department heads looking to implement data lake solutions in their own organizations. Rather than just picking a single vendor who then provides everything, they could use several to handle different chores and get the best of all the available options. For instance, Amazon Athena-based technology may query information in a lake directly while IoT and log processing could be handled by something based on the Lucene library like Elasticsearch.
Some specialists might even wish to introduce Splunk or other related solutions into their custom data lake layouts. The wide variety of off-the-shelf projects has helped to dramatically reduce the number of individuals who have to write custom solutions, which makes it easy to get up and go with a new lake. Since there’s no need to convert information into a specific format, the implementation phase is usually much smoother.
Regardless of which specific solutions they elect to go with, however, it’s likely that an increasingly large percentage of the data processing market will look to data lakes. They’re quickly proving themselves to be both flexible and at least relatively easy to roll out.
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