As the online world becomes more interconnected and congested, the urgency for software developers to implement Big Data has become more pressing than ever. It’s an unmistakable fact that cataloging and interpreting information through new technologies is becoming an essential aspect of competition.
Businesses seek to outperform each other in effective ways, How? Engaging with the wealth of data left by consumers seems like a good way to achieve this. The technological world has seen some of its most radical changes in just a few years. If we look at Statista’s reports, their estimated total market value for Big Data is set to grow to $100 billion by 2027. Early adopters of these trends and technologies will most likely benefit from this – and the race is on to see which industries come out on top.
Rapid tech adoption across many industries has led to many seeking out quick and easy fixes to a lack of expertise, of course. Managing data is part of the development process, but despite the overwhelming exposure big data analytics has gotten, not many individuals understand some essential terms – namely, differentiating the uses of data lakes and data warehouses.
The Problem
The tech world lends itself to a lot of pernicious habits, such as adopting deficient development practices. This happens for many reasons, but many small and medium enterprises fall on these habits due to lack of development time: you can’t be early to the race and also be the one with the best horse, so to speak.
Overall, the demand for technological skills will keep growing. According to the Monster annual report, Big Data Analytics is poised to become the most in-demand skill this year. An estimated 96% of companies in the tech industry plan to invest in hiring the appropriate staff, and this surge is likely to affect other unrelated sectors. Cryptocurrency, B2B and BSFI will certainly incorporate data-related skills into their workflow.
This mass adoption of technological tools has its limitations, of course. The talent-gap will of course rear its head, and many small-sized companies will be left without the expertise in handling many useful terms and technologies for their business. The difference between data lakes and warehouses is one common issue, one that can affect development cycles in the long term if not addressed.
The Fix
In order to reach a profitable tech-based future, software companies and web developers will need to stop planning independently and begin to adjust their strategies towards cooperative efforts. When they operate independently, they miss out on key elements and the opportunity to improve themselves.
An example of this comes when we talk about storing our data. Many analysts continue to debate the distinction between a data lake and a warehouse because at a first glance, they look exactly the same. However, both types of data storage have their specific advantages. So treating them as interchangeable assets could lead to disastrous results.
In lieu of this issue, we must then consider the difference between data lakes and data warehouses. After all, the best way to gain an edge in the market is by optimizing our basic understanding of the tech world.
The Differences
Even if they’re both considered data storage tools, lakes and storages are not interchangeable. Essentially, when we look at what these two can do, we must decide between raw data and defined, filtered data. While a data lake stores vast amounts of unprocessed data, a warehouse will store its refined counterpart.
They both have their uses, and understanding the purpose behind them is essential.
Raw & Processed Data
Storing raw, unprocessed data requires bigger storage capacity compared to refined information. Lakes store all kinds of data. It doesn’t matter if it’s going to be of any use in the future or not, they’re made to store it.
Since this data is made to last forever, it can be analyzed at any point in time. This makes it extremely useful for machine learning, which needs easy access to data. The malleability present in this same type of data also helps in making the whole process faster. Coupled with the fact that cheap servers can usually scale these storages from terabytes to petabytes efficiently, the whole venture can be very economical for startups.
In contrast, a data warehouse presents highly structured data models, built for answering specific queries and needs. Reporting and profiling are well-suited for these tools, as they simplify a lot of complex functions.
Traditional & Non-Traditional Data
Determining the purpose of the data being used is also a key differentiating factor. A warehouse, with its specificity being the main outlier, will often feature “traditional” data. This information comes from transactional systems and consists of quantitative data. Storage space isn’t wasted on data that will never be used, so things like images and social network activity will most likely be ignored.
Data Lakes focus on unused, non-traditional data instead. The raw data presented will only be transformed once it is ready to be used. In the meantime, most servers will be able to maintain it in its raw form. Healthcare greatly benefits from this, since most of the data in this sector could be cataloged as non-traditional. Logging reports, clinical data and surgeon notes are difficult to specify, so they’re ill suited for data warehouses.
Accessibility
Open access to data sounds good on paper, but it isn’t always the case. Evidently, a specific model, such as the one offered by a data warehouse will always be preferable to non-technical users. Accessibility plays a huge role in deciding which type of storage you would prefer. This since, sometimes, your workers might prefer to do a little more analyzing.
Basically, if your company is composed of operational workers, you’ll most likely favor a data warehouse. This, because they need constant and easy access to reports and statistics. Out of the two, it’s the most accessible, being favored in businesses such as finance and commerce.However, certain areas in the tech market possess skilled workers (such as data scientists) that know how to navigate raw and unstructured data packs.
A data lake is a versatile type of data. So it only needs experienced users to incorporate it into their workflow. For example, transportation uses data lakes for predictive analytics, using it to reduce costs down the transportation pipeline.
Invest In Knowledge
It’s important to understand the market before jumping in. We at Teravision Technologies have over 18 years of experience researching and understanding the tech world. If you need guidance, or would like to learn more about how we can help you invest in the appropriate resources, contact us.
