Analytics Data science

What are the Challenges Addressed by Data Lakes?

Challenges Addressed by Data Lakes

While there is no disagreement on the fact that data lakes can be really beneficial with the amount and variety of data they can store. Data Lakes can address certain challenges to prove their full potential before they are unanimously accepted. With the right strategies and resources in place, it can be a good decision for your business and bring in a high return on investment.

Data lakes are disrupting the way how data is stored, accessed and analysed. Instead of storing it in silos such as data warehouses where the data is processed and classified to find a place. Data lakes store data and come without changing its original form.

This data can be processed in multiple ways to derive valuable insights. Collecting, storing and processing data of this magnitude, intensity and complexity create various “big data” problems. 

With the advent of data lakes, storing data of all types has become much easier. Data Lake is equipped to handle structured, semi-structured and unstructured data from the non-traditional sources and formats such as web server logs, images, videos with no upper limit on the file size. 

Although Data Lakes became extremely popular because the idea of not losing any data seemed attractive to organisations. They started losing sheen as the challenges associated with them started surfacing.

Organisations found it daunting to manage systems that could work on such huge databases. However, the fact is that data lakes can be extremely beneficial when managed properly. 

Challenges Addressed By Data Lakes

The features of data lakes that make them exceptional can also turn into a challenge. But with careful implementation, these challenges can be addressed sufficiently.

1) Variety of Data

Data lakes accept and store data in a variety of formats from multiple sources in its original form at a single location. Data lakes use ‘schema of reading‘ which means the data is stored in an as-is state and worked upon only at the time of access.

This state of “disorderly storage” can make it a challenge to access the data when required. But with efficient data lake solutions and machine learning models, this data can be put to good use.

With 2.5 quintillion bytes of data being created every day, a data lake has to be robust enough to handle the magnitude. Efficient data lakes are also easily scalable to accept even more data in their repository and yet function smoothly.

Data lakes are also flexible enough to process data as per the requirement of the business function. The same set of data can be used by different departments to reveal different parameters.

3) Centralized Storage

Data lakes, by design, collect data from multiple sources and bring it to a common pool. The biggest challenge is to make that data available as and when required. The centralised storage in data lakes has to be integrated with efficient solutions to ensure that data can be extracted for different purposes and at different points in time.

4) Separating Compute from storage

Forrester estimates that 60-73% of the data stored by companies is not utilised for analytics. To derive valuable insights from the stored data, compute needs to be separated from storage. Cloud data lakes enable separating the two and make each one scalable on their own, thus providing a more cost-efficient solution.

5) Data Governance and Data Management

Not having a good governance system in place puts data at enormous risk. A robust data governance system ensures that the data is protected, managed and easily accessible to data teams without compromising on the quality of the data.

Sound governance of data lakes enables assessing the data for usability, identifying data ownership, tracing data throughout its life cycle and allocating appropriate data definitions to know the right value of data element.

6) Effective Metadata Management

Not being able to leverage the data defies the whole point of storing it in such huge amounts. Data lakes rely on effective metadata framework to optimise the value of the stored data. The metadata management system simplifies, automate the data tasks and make it available to users across the organisation based on their requirement.

7) Data Security

It is imperative that such massive data is prone to threats at various levels. Any breach of security can lead to catastrophic implications. Securing the data at multiple levels is crucial.

Data management systems should address security issues both while storing and accessing the data. Data encryption, role-based access control, authorisation and authentication should be used strategically to protect data.

8) Big Data Analytics and Data Mining

There are immense opportunities in big data analytics based on data lakes. Various types of analysis- descriptive, diagnostic, predictive and prescriptive – offer valuable insights into business trends, consumer behaviours, problem-analysis and demand projections.

The inputs needed for such detailed analysis are provided by various machine learning-driven data mining techniques. Unstructured and raw data available in data lakes can be leveraged to benefit the business using advanced data analytics and data mining techniques.

Wrapping Up

Data is everywhere, but it needs to be harnessed to derive its full potential. Data lakes offer huge potential for treating and using this data. If handled strategically, they have immense potential and address many challenges associated with big data.

Businesses can optimise data collected through different resources. Reliable infrastructure to collect, store and retrieve data coupled with frameworks and techniques to derive valuable inference can actually work wonders for a business. 

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