Everything Data Scientists Should Know About Organizing Data Lakes

Everything Data Scientists Should Know About Organizing Data Lakes

Srihari Sasikumar

Last updated July 9, 2018


Data lakes have been a hot topic lately due to a lot of untapped information stored within different kinds of data repositories. In addition, the companies that use data lakes are outperforming other companies with 9 percent revenue growth. Organizations need to seize all available opportunities to create value from their data, and data lakes can help them to utilize different approaches to analytics in order to really accelerate decision-making capabilities. 

In a Simplilearn Fireside Chat,  Simplilearn's Chief Product Officer Anand Narayanan and Big Data expert Ronald Van Loon talked about data lakes, why they are useful to data scientists, the differences between data warehouses and data lakes, and how data lakes can help with governance and privacy. You can listen to the Fireside Chat to learn about data lakes or read a summary of it below. 

What Is a Data Lake?

Simply put, a data lake is a storage repository that holds a vast amount of raw data in its native format until it is needed. Picture a data lake as some kind of container for massive quantities of data of all varieties, where there's no need to alter any of the data before you store it. Data is stored just the way it is, whatever the form. Data lakes store relational data from business applications, normal relational data sources, IoT devices, social media, mobile apps and all other kinds of sources. 

Businesses can use data lakes to store data for future use until it is needed to help improve customer insights and support the customer experience. In addition, organizations can start using Artificial Intelligence (AI) applications with natural language processing. Data lakes can also help to support Big Data initiatives and help companies leverage massive volumes of data in a consistent way. Combine this with machine learning algorithms, and you can start doing real-time analytics with those huge amounts of data sets.

The Difference Between Data Warehouses and Data Lakes

A data lake differs from a data warehouse. While a hierarchical data warehouse stores data in files or folders, a data lake uses a flat architecture to store data, but the difference is much more than that. 

Data warehouses store large quantities of structured data in a very well-organized manner. Businesses can extract, alter and improve information and data can be used as needed. Therefore, the data quality in the data warehouse is much higher and more reliable compared to what’s stored in a data lake. It has already been structured and it's ready for a data analyst to use for all different kinds of applications. 

Data lakes differ from data warehouses mainly because of the different types of formats they can house and because that data does not have to be formatted first. As a result, data lakes are relatively cost-efficient because of the nature of the data and because this is an inexpensive format. Businesses are able to manage this to house the data lakes for longer for future use, and the data can be extracted at any time depending on the purpose. 

For a very simple comparison, a data warehouse is like filtered water that is safe to drink while a data lake is like untreated water that needs to be treated before you can drink it. Basically you have pre-treated data that is used for specific purposes vs. unstructured, almost random source data that you use for whatever purpose.

The struggle to access, use and understand all of huge volumes of data is why businesses are turning to data lakes: They cannot handle this type of data in this volume and variety in data warehouses anymore. The data lake is the way to implement it and for companies to change their culture to create a data-driven culture, allowing them to create all different kinds of analytics processes and new types of knowledge processes. 

What Is Needed to Use Data Lakes?

Integrated tools like Hadoop are used in these types of large environments and can help business obtain value from their raw data in a way that it integrates with other data warehouses. Data lakes can enable businesses to utilize and to analyze data that wasn't readily accessible before. However, if businesses don't ensure that exporting is useful and relevant, the data provides little value. The right tools need to be deployed to help organizations manage their data later. 

An organization’s ability to derive value from the data lake is dependent on a lot of factors. For example, what types of development tools and processes and methodologies are being used? Are they traditional? What's the legacy? Or are they new? Analytics tools play an important role because they need to be refined to the process for the different complex types of data. The workloads manageability is also an important factor. So is the amount of data users need within the business environment and how fast the data needs to be accessed. Does it need to be real time? And for what type of application will it be used? And regardless, if the data is compromised or damaged, the data lake is not valuable. It’s like the saying goes: garbage in, garbage out. 

If the data isn't really integrated or properly modified, an organization will face challenges and analysis will take longer. You need to do a lot more data preparation yet organizations need to be aware that many forms of cleansing, enrichment and standardization might compromise the data the data lake and cause some information in this repository to lose its value and its usability. Businesses need to treat the data in a data lake as though the data could have high potential for providing valuable insight. 

But on the other hand, we have all this volume as well. Everybody keeps storing everything because they think it can be valuable in the future. But because it's uncertain as to what exactly the data may contain and how it can be valuable, it shouldn't be organized in a simplified way with like objects. Potentially useful available data may be thrown out or may be lost otherwise. Or a data lake can become polluted when trying to use too many different tools on the data lake. This creates a lot of data that doesn't have proper structure or else the organization doesn’t have a proper quality control process or the other data process. Then it becomes time-consuming and hard to find the right type of data. 

Data Lakes and Governance and Privacy

When talking about any kind of data today, one must also talk about governance and privacy, especially now that GDPR is in place. Businesses must govern and organize the whole end-to-end data management. When it comes to data lakes, the challenge is that you can store everything but the question is, are you allowed to? Do you have consent to store that data? In addition, organizations have to maintain data protection standards and take steps to ensure that data is kept safe. If they don't have the right type of data management tools in place, they may not be effectively controlling and monitoring data in the data lakes like they should. 

Getting the Most Out of Data Lakes

Moving forward, businesses need to implement end-to-end data management best practices so they can prevent data lakes from growing unmanageable or even turning into massive data silos. This will be a trend for the coming year. In addition, businesses need a strong data-driven management that focuses on turning this raw data into insights through a systematic process that's built on automated in-depth intelligent technologies like machine learning and deep learning. This kind of technology can help an organization find the right data, clean it automatically and make it ready for your application. 

Data is needed to support decision-making on every level, and a data lake can help to provide that data as long as you have smart data management practices in place. 

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About the Author

Srihari Sasikumar is a Product Manager with over six years of experience in various industries including Information Technology, E-Commerce, and E-Learning. Srihari follows the key trends in Big Data, Data Science, Programming & AI very closely.


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