What is a Data Lake?

A data lake is essentially a bottomless repository that you can fill with data without having to consider its value or format. Data lakes can store as much unstructured, structured and historical data as a user needs, which makes them excellent for storing data that you may need in the future.

That said, just because data lakes offer so much freedom doesn’t mean that businesses should simply dump all their data into them. If they do, they risk their data lakes becoming one more thing contributing to today’s big data problems. Here, we break down how to keep your data lake organized to ensure that the data that it houses stays intelligible, useful and relevant.

The Data Lake: Why It’s Useful For Businesses

In a data lake, data is stored in its original format or after undergoing a very basic “cleaning” process without being transformed or integrated with other data sources. Because of this, data lakes are able to store a wide range of data, including both totally unstructured and highly structured data. This kind of unstructured storage - like Amazon S3 - is relatively cheap, making the lakes a comparatively inexpensive option for many businesses.

Additionally, as a result of the cheaper costs, data can sit in a data lake indefinitely. Then, when you decide to extract that data from the data lake, you can determine the format you need the data to take.

This level of freedom makes data lakes highly adaptive places and allows for a broader range of analysis to be done on the data that’s stored in them. Specifically, data lakes allow data scientists and users to analyze data that wasn’t previously accessible i.e. social media posts, customer support call notes, etc.

This makes data lakes a potentially fantastic tool. However, this is only possible if that data isn’t compromised in some way. Furthermore, since the data is neither transformed nor integrated, this analysis will generally be more time-consuming than it would be in a more structured warehouse.

So, how do you avoid these potential problems and make the most out of your data lake? Let’s break it down:

Building a Successful Data Lake Architecture

In a traditional data warehouse, data is transformed, standardized and consolidated so that it’s ready for analysis. While this works for warehouses, this same kind of cleansing and standardization could actually make you lose some useful information in a data lake.

Think of it like a store’s warehouse versus an huge, unmarked storage space you’ve forgotten about: you already know, more or less, what inventory is in the store warehouse, and organizing it will just make your job easier and more transparent. The unmarked storage, however, is a different story. You don’t know exactly what’s in there or how valuable certain objects may be. This means that, if you simply organize like objects and get rid of outliers, you might be discarding the very treasures and nuggets of information that could be useful to you.

To get it right, then, you have to learn how to control the chaos and build a data lake architecture that is flexible and logical, a.k.a one that you can use down the line to gain actionable business insights.

Related Reading:

The Ultimate Guide to Data Architecture

Data Lake vs Data Warehouse: 7 Critical Differences

Here’s how it’s done.

1. Decide on Your Data Lake Architecture

Data lakes are typically created using a combination of some kind of big data platform - like Hadoop - and homegrown tools. Many businesses, for example, opt for a hybrid architecture that combines Hadoop and a relational database. This makes sense for businesses that have relational data warehouses - which, at this point, is the vast majority.

SEE ALSO: Data Lake vs Data Warehouse: 7 Critical Differences

When deciding how to start creating your data lake, then, it’s important to consider what will work best for your business. Do you have any current data that isn’t being fully analyzed? What’s falling through the cracks? What source is putting out a lot of information that you’re not fully utilizing right now?

Historical data, specifically, can sometimes contain hidden insights. For example, a support ticket goes through a set of statuses while being processed - from when it is first opened, to working, to eventually being resolved. It might even have further status changes after resolution, like product review, product feature on the road map, feature development, and general release into production. Because a data lake has few limitations on storing historical data, then, a single support ticket would have many records in the data lake, one for each of these status changes.

For historical reporting, this comprehensive historical view of the support ticket could be helpful in accurately evaluating the quality of service while the ticket was being processed. If you’re looking to improve customer service and user experience then, something like this may be good to analyze. And this can really only be done with a data lake - information like this may be lost in a data warehouse, where it is important to only store the pertinent data.

The goal then, is to have a firm understanding of your businesses’ larger analytical efforts and needs - and to use your data lake to help you with those bigger goals.

Once you choose the kind of data lake architecture that you want to build, there are many best practices to follow for proper deployment and implementation.

2. Follow Data Lake Best Practices

Avoid Dumping Useless Data

Because of the extreme flexibility that data lakes offer, many tend to simply “dump” the data that they don’t need at the moment without considering the organization or the structure of the data. This can make it extremely difficult for users to navigate the data lake down the line, thereby increasing the likelihood that the stored data will go to waste.

SEE ALSO: DATA HOARDING: A BAD HABIT COMPANIES NEED TO OVERCOME

Establish a Governed Process and Set Up Data Lake Zones

To avoid this, implement some controls, including a governed process for introducing new data into the lake. While the data shouldn’t be cleansed or transformed, there should be a process. Remember, keep your bigger goals in mind when defining this process to ensure that your data lake works for you.

Next, an effective practice is to set up zones within your data lake to keep the information organized, logical and accessible. A standard zoning procedure includes:

  • Transient zone: used to hold short-lived data like temporary copies
  • Raw zone: where raw data will be maintained and sensitive data will be tokenized, encrypted or otherwise secured
  • Trusted zone: once your data is vetted for quality, validated, etc., it becomes the “source of truth” in this zone
  • Refined zone: this is where manipulated and enriched data is kept

These should be either physically or logically separated - on different servers, with specific access privileges, etc - to help maintain data lake cleanliness.

Maintain Data Privacy and Security

On the note of access privileges, make sure to always establish security measures like role-based access control, authorization and encryption of data. This kind of restriction can help protect the data that’s in its most raw form and prevent any confusion or disorganization.

Track Changes

This is likely the most difficult - and the most necessary - best practice. As your data lake grows and you continue to get new information from various sources, it will be increasingly important for you to maintain the relationships within and between your datasets. This will be a complicated process, requiring you to both track your activity logs - who made a change in the data lake, when, and what they did - and your system a.k.a how datasets and fields are related to one another. This includes monitoring all your sources and keeping tabs on essentially anything that touches your data.

Even if you don’t do this perfectly 100% of the time, the key here is to do the best you can: even incomplete tracking can help you fill in the gaps and maintain your data lake’s flexibility and extensibility. That means that it will always be useful and that it won’t turn into a bad data problem.

Overall, the goal with your data lake is to be systematic, logical and clear. While this may seem counterintuitive - given a data lake’s inherent freedom and flexibility - it’s essential if you want it to be a tool rather than a hindrance.

If you’re not sure where to begin - or you would like some assistance - feel free to reach out. Integrate.io’s experts can help you with the data storage and integration process so that you can concentrate on your core business efforts.

Start a conversation with one of our experts today.