Data Lake: A Comprehensive Guide

The expanding network of technologies and introduction of concepts like Internet of Things (IoT) have had a huge contribution to data coming from different processes. And therefore, the need to curb the storage part of ever-growing data created a need for keeping information on Data Lake.  Besides, the introduction of data lake enabled businesses to store data with greater ease while giving the ability to run analytics on machine-generated IoT data which ultimately helped cutting improving quality of the operations at reduced operational costs. The process even involves introduction of end-to-end data warehouse testing strategy within the enterprise.  

Additionally, data lakes allow users to store relational data from operational database along with the data from business applications and other non-relational information that comes from social media, mobile apps, etc. Moreover, data lakes bring the ability to understand what data is crawling or being indexed or is in the catalogue.  

But What Exactly Data Lake Is? 

Data lake can be defined as a central location that contains large amount of information in its raw form. Unlike the conventional hierarchical data warehouse, data lake runs on a flat architecture and object storage.  Object storage contains data with metadata tags and an identifier that enables data location and retrieval across regions to deliver enhanced performance. Also, the inexpensive storage and open format that comes with data lake makes it very convenient for applications to harness maximum out of available data.  

Why Data Lakes Were Introduced? 

Originally, data lakes were developed to compensate on the expensive data warehouses that delivered scalable analytics but lacked at handling modern use cases. Data lakes are usually worked out to consolidate the organizational data in a central location without any use of schema.  

Moreover, it allows storing data at all stages of refinement process, while allowing ingestion of raw data along with the tabular data sources as well as intermediate data tables that are made during the process of refining raw data.  

In other words, most database and data warehouses, data lakes could work on all data types including structured, unstructured, and semi-structured information such as audio, documents, images, and videos, which makes up to the information that is critical for machine learning and advanced analytics.  

Why A Data Lake Is Used?  

To complement modern data architectures, data lakes are made to support open format which allows overcoming lock-ins with the proprietary systems. On top of that, data lakes are more durable and offer more scalability and object storage at lower costs, while giving the advantage to ingest on variety of data formats making things more convenient. When architecture properly, data lakes could allow:  

• Foster Machine Learning & Data Science Initiatives 

Data Lakes could be harnessed to create structured data for SQL analytics while cutting off any latency. Besides, they could be used to retain raw data for machine learning and analytics projects.  

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• Centralization & Consolidation 

Data Lake helps overcoming the issues surrounding data silos such as data duplication, security policies, and collaboration. In short, data lake makes it easy for users to locate any data with centralization and consolidation. 

• Data Source & Format Integration 

Data lakes makes it very convenient for the users to retain the variety of data, right from binary files to image, video, and other forms of data, keeping your data source updated.  

• Data Democratization 

Data lakes are flexible and therefore allow users with different skills to work through tools for execution of analytics tasks.  

What Challenges Do Traditional Data Lakes Possess? 

Though data lakes have their own benefits, there are some very significant limitations associated with the concept. These can be identified as missing features such as no support for transactions, no data quality governance, improper performance optimization efforts, etc. Such issues could make any data lake turn into a data dump.  

• Limiting Reliability  

Lack of tools could make a data lake suffer with reliability related concerns, making users struggle on reasoning of data. These issues could emerge from the hassle of combining streaming data, batch combinations, and data corruption. 

• Poor Performance 

The increasing size of data lake could trigger slowed performance on the traditional query engines, which further leads to metadata management, data partitioning, etc. 

• Missed Security 

Data lakes are hard to govern due to visibility issues as well as minimum access to delete or update data. These limitations surrounding security in data lake makes it difficult to work on regulatory requirements. 

In short, traditional data lakes are not so sufficient when it comes to meeting the business innovation needs. Thus, the business organizations operate using complex architectures and siloed data. These usually include data warehouses, databases, and storage systems established within the enterprise. 

However, the modern-day data lakes are all about simplifying the data architecture and combining all the data to pursue the futuristic goals surrounding data analytics and machine learning.  

How An Advanced Lakehouse Could Solve Those Challenges? 

Though it may appear to be a challenging task to work with traditional data lake, a contemporary approach to data lakes could change things for better. For instance, addition of transactional storage layer on top or using similar data structures and data management features in the data warehouse while running them directly on cloud data lake. Such practices not only allow analytics and data science to coexist with machine learning in an open environment.  

Ever wondered how database testing complements quality assurance goals? 

Read Here: Leveraging Database Testing to Transform Quality Assurance 

In other words, a Lakehouse could make way for wide range of use cases, varying from BI and ML projects to enterprise analytics. Besides, the data analysts could work on querying the data lake using SQL while enriching the data sets to more accurate ML models.  (Check image to know what other use cases data lake possess) 

Furthermore, data engineers could work on creating automated ETL pipelines while engaging BI analysts on creating visual dashboards for faster reporting. All these defined applications could be leveraged simultaneously without making any changes to the data while making the new data stream in.  

Lakehouse Best Practices To Follow 

Lakehouse could be worked to store all the data into a data lake without making any efforts in transforming or gathering the information or preserving the data for data lineage or machine learning objectives. However, to harness maximum benefit of Lakehouse, there are certain best practices that are required to be worked: 

• Data With Private Information Should Be Masked 

Any information that falls under the category of PII or Personally Identifiable Information needs to be pseudonymized to comply with GDPR goals and ensure the data could be saved indefinitely.  

• Role & View-based Access Controls 

Access control allows an organization to attain more control on security. Thus, implementing both role and view-based access controls allow better tuning of the entire system.  

• Delta Lake For Added Performance 

When working on big data, it often becomes very difficult to attain improved reliability with database and therefore implementing Delta Lake could enable added performance.  

• Using Data Catalog With Data Lake 

Lastly, enterprises could invest in metadata management tools and data catalog that could be used during point of ingestion to enable self-service analytics.  

Quality Assurance & Data Lakes 

When it comes to big data projects, quality is one of the most important components. However, integrating quality assurance and software testing into a Data Lake is quite a different process. The process involves building counter systems that can validate both raw data and aggregated data to fold correctly.  Besides, the testers even need to work on overcoming any count checks, mismatches, missing data, simulation of data sets, etc. In other words, ensuring the quality of the data lake require testers to work on following factors:  

• Data validation & accuracy 

• Integrity of process 

• Performance & volume testing 

• Infra testing for DevOps validation & service configuration 

Therefore, working on QA aspect of data lake require testers to have a software development engineer’s perspective. From understanding the programming constructs to automating only the tasks that are vital.  Need help upgrading from your conventional data lake implementation practices with quality assurance? Our experts could help you meet your goals.  Contact us today!