3 questions you need to ask before implementing a Data Lake

Data Lake Implementation-CloudMoyo

What does it take to successfully implement a data lake? – Well, the answer is having a clear idea of what you aim for or why you need a specific set of data from data storage. If you’re are thinking whether or whether not to implement a data lake, here are the key questions you must ask:

  • The first and foremost question is how big the problem is? What kind of data can help you to address that problem, what kind of data you don’t need to save, etc. This will also help you know how you can accomplish with the stored data.
  • Is the data transactional or non-transactional? If the data is non-transactional or a mix of both then data lake is the right option for you.
  • What would be the best technology platform – on-premise or a cloud data lake?

Data Lake at a glance:

Choosing the right model of data architecture is crucial. The first thing to know before opting for a data lake is to understand what a data lake is? How is it different from a data warehouse? Is it the right model for your enterprise?

Well, data warehouse is a data architecture that necessitates on having only structured data in a tabular format while data lake allows the storage of both structured and unstructured (it can be a ‘messy’ combination of audios, videos, images, other data information, etc. in its natural format) in one storage/repository. A data lake has the capability to serve a number of data analytics.

In other words, data lake is a storage or a repository that stores data from disparate sources, generated in high volume, variety and velocity. This gives an enterprise flexibility to think on how a specific set of data can be used.

Also Read: Difference Between a Data Warehouse and a Data Lake

Role of Machine Learning

Machine learning helps in finding patterns and assists an automated analyst with determining what to do with the specific pattern of data. Machine learning provides you an option to analyze the data in the data lake itself.

Due to lack of skills and talent on board, most enterprises stumble upon the idea of developing a machine learning strategy after accumulating billions of data. Remember, billions of unnecessary data can sometimes turn a data lake into a data swamp.

It turns out to be frustrating in driving insights from a data lake without proper approach and right data strategy.

Also Read: How to build Enterprise-class Machine Learning apps using Microsoft Azure

Listed down are the three considerations you need to take before implementing data lake as this will give you a clear idea of whether a data lake is a right approach or not:

  1. Data type: As mentioned above, a data lake consists of all types of data- structured and unstructured, if you want to gain insights for this type of data, then go for a data lake without giving it a second thought. On the other hand, you might want to stick with a data warehouse if you are going to work with much structured, traditional data in a tabular format.
  2. Need for data: Do you just want to store a data to analyze it later? This is the core tenet of a data lake. Unlike data warehouse, data lake provides the flexibility to use a stored data for later use. The advance structuring of data not only requires a high cost of investment but also limits the repurposing power of any data in the future for new use cases. A data lake could be a good fit if you want to provide a higher level of flexibility for your future BI analysis.
  3. Skills and tools: A data lake typically requires significant investment in big data engineering. A big data engineer is difficult to find and is always on high demand. The data lake approach might prove difficult if your organization fall short of the skills of a big data engineer.

Data lakes are often criticized as chaotic and impossible to effectively govern. Whichever approach you choose, make sure you have a good way to address these challenges. It is advisable to start small. To gain proficiency in this landscape, you must start with a smaller data lake instead of kicking off the enterprise-wide lake. You can also use the data lake as an archive storage and let your business users access the stored data like never before.

Also read: A deep dive Into the Microsoft Azure Data Lake and Data Lake Analytics

You can use these top three considerations we have posed above as a general guideline for deciding whether your company or organization should be thinking seriously about building a data lake. If you want to know the difference between a data warehouse and a data lake, read this blog.

Talk to us and learn more about Azure Data Lake, Azure Data Warehouse, Machine Learning, Advanced Analytics, and other Business Intelligence tools.

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