How To Build a Data Lake The Right Way : Big Data

Data-driven companies are riding on rapid business growth with cloud data lakes. Cloud data lakes are helping new business models and real-time analytics in order to support better decision making. But, as the workload migrating to cloud data lakes increases, companies are forced to address the issues of data management. 

Today in this blog we will discuss how to build a data lake in a correct manner?

Transactionality on data lake

Organizations don’t use data lakes as  cold stores anymore, instead sources for ad-hoc analytics. Data lakes have evolved drastically and are using business intelligence. 

In order to create a reliable analytics platform that can supported the expansion of use cases, data engineers need to make a mechanism to build-

• Dimensions (Type- I and type- II)- This is one of the common requirements for any data analytical platform and needs certain capabilities to INSERT and UPDATE data. 
• Data restatement- Organizations are integrating data from various sources like CRM, ERP and many more. The issue with them is that this can cause incorrectness or poor quality of data. This data needs to be rectified in subsequent steps. Businesses require clean, complete, accurate and up-to-date data that increases the resentment of data.

Security & Privacy regulations & compliance

Everyone of you must have heard about “Right to be forgotten (RTBF)”. The new global data privacy regulations have made it possible to erase their unwanted information. These regulations govern consumer’s rights on their data and levy hefty penalties for non-compliance. Financial penalties are significant and cant be looked at and businesses are now facing the challenges to fulfill the data privacy and protection requirements and making sure of the business continuity. RTBF enables deletion of a specific data that resides in a data lake. It gets really hard to delete specific subsets without hampering existing data management. There are new solutions but not all guarantee complete satisfaction. And hence, organizations are still building customized solutions to fulfill new requirements. But these solutions also have problems related to updates, maintenance and auditability.

Faster insights

We know that distributed systems have latency issues when it comes to completing writes. But apart from that, they also have additional overhead. The overhead stems from writing to staging locations prior writing to cloud storage. They update the entire partition instead of a single record. This creates a huge impact on overall performance and becomes a matter of concern as organizations are now operating data lakes at a very large scale.

Data integrity and consistency

For any data lake, concurrency control is must to offer support to multiple users, and applications and thus there are high chances of conflicts. For example, to make sure of data consistency, integrity and availability one user wants to read from the same file or partition or maybe two users want to write to the same file. A modern data lake architecture should be created in a way that it can address such scenarios. It also needs to make sure that these operations do not violate accuracy and completeness of data which can lead to erroneous results.

Choose the right compute engine and cloud

Now-a-days there is a rapid growth in demands for insights and information and it has resulted in a dynamic increase in data collection and storage. The data collected needs to be harnessed in order to improve customer experience and thus require business to adopt a data architecture that can serve various use cases all while preserving the choice of data processing engine and cloud infrastructure to better serve use cases in the future.

At Ksolves, we have given the utmost priority to these considerations for our data platform’s design-

• It supports full transactionality irrespective of the cloud we use, whether it is AWS, Azure or GCP.
• It offers built-in support for delete operations and forces customers to comply with all the regulatory and privacy requirements for “Right to Erasure”.
• It eliminates extra overhead as you can directly write to cloud objects. It also guarantees data integrity with the best possible performance.
• You can have full freedom to select the best data processing engine like Spark and many more. 

Ksolves big data services

Ksolves is one of the best and most advanced big data service providers across the globe. Our services like Apache Spark, Apache Hadoop, Apache NiFi etc stand out in this competition. We offer budget-friendly services with low latency and high throughput. Our qualified and experienced big data developers had impressed many clients with their timely deliveries. We know what it takes to build a company and offer best big data solutions for better growth of organizations and boost business solutions. 

If you are looking for more big data requirements or information, write your queries in the comments section below or give us a call to book your free demo now.