Enterprise Data Lake Solutions

There is a series of evolution with how mankind stores data. From storing data in the papers to migrating data to the cloud, we have seen many phrases! But Data Lake is the epic discovery for the tech world which offers a lot more than storing and securing the data. Enterprise Data Lakes capable of storing tremendous volume of data and lets you unfold the insights and hidden pattens within it. Let’s get started. 

What Is Data Lake? 

Enterprise Data Lakes are fundamentally designed to store unlimited data. The data stored can be any type like Structured, Semi Structured and Unstructured which grabs the data from diverse resources. Data ware house can be accessed by people with limited technical knowledge but handling data lake requires deep skill and knowledge. Thus, Data Scientists and Data Engineers are the end-users of data lake. 

There is no predefined purpose for data ingestion in the data lake, anything can be stored and used by organization for multiple purpose. It contributes much more to the organization with its embedded intelligence and profound planning via high tech search and analytical techniques. 

Things To Consider Before Getting Started 

 Before get into data lake solutions do consider the below things. 

Technology: As of now, Apache Hadoop is the most popular architecture among the organizations. Yet there are many more good alternatives are available. Check which one is good for your business. 

Security: In business, if data is lost, everything is lost. You must have to provide proper protection for data lakes against hackers and unauthorized access. 

Cost: Analyze and compare the billing of vendors and then decide. 

Data Monitoring & Management: Look for work load management capabilities and a fortified UI for system administration 

Data Processing & Scalability:  Check out the list of tools, how data exploration will be enabled and the background processes of the platform 

Data Governance: Ask yourself like does the ensures weather the data is consistent and reliable. 

Data Analysis: Have a look the mechanism behind the analysis of the data and how flexibly you can ingest data.  

How To Implement Data Lake? 

There are prime steps associated with implementing data lake. 

• The very first thing would be defining the new organizational roles and structure. 
• To perform a perfect data implementation strategy, your organization have to be clear with goals, milestones and precise plans. Should have the criteria to measure the success of data lake implementation. 
• Ultimately, all formats of data generated by the organization are ingested to the data lake. But there should be the prioritization in identifying the high-level data. 
• You should ascertain whether the information is currently being evaluated and at what level. Despite being a potential source for ingestion, well processed data may not be as important as data from untested systems. 
• Data governance strategy needs to be implemented and ensure the data security and accuracy. 
• Create guidelines for the exploration, experimentation, and analysis of data. To review the data and choose the use cases that will be most valuable to the business, data scientists should adhere to a uniform yet flexible procedure. 

Also read: How Cloud Computing & Machine Learning is entwined with each other.

Sensitive Data Classification 

Dealing with the sensitive data is the most vital thing to perform in the initial stage. if we are unaware of sensitive data, then the organization will face serious issues in the future. Based upon the sensitivity of the data, Security methodology classification needs to be done. Levels are determined by industry and regulatory security standards as well as by the consequences for the organization of unauthorized data disclosure or modification. 

Administrators are able to implement the proper baseline security methods and procedural controls based upon the security classification. Organizations must assess the data already in the data lake and create processes for analyzing incoming data in order to assure correct classification. 

Don’t compromise with security! 

Data Lakes are not the island! Well trained hackers look for all the possible ways to corrupt your data. They will search for the ways to gain access for the input files, work files and the backup files too. Best security practices should be implemented on all the possible ways. 

Prominent Data Lake Tools 

AWS Lake Formation: It provides single and unified space for data storage. The tool offers advanced security features, accurate data authentication. Ideal for large scale queries 

Azure Data Lake Storage: offers simple solution to build data lake. Seamless integration with analytics and ML. The tool creates searchable data catalog with an audit log for identifying data history.  

Qubole : Stores Data in open format which can be accessible with open standards. hoc analytics reports, data pipelines for real-time insights are the key features to specify 

Infor Data Lake: Gather data from all the possible resources and ingest into data lake instantly. Because of the careful classification, the information kept here will never become a swamp. 

Intelligent Data Lake: This tool lets customer to get fine value form Hadoop-based data lake. Because of the Hadoop system’s foundation, users may conduct complex data searches without having to know much code. 

Top benefits of Data Lake 

Rich Insights: A data lake does not filter any data, in contrast to data warehouse ETL. In actuality, all data is sent straight from the source into the lake. As a result, data lakes become an extremely inventive, perceptive, and abundant source of knowledge. 

Affordable Cloud Solution:  For enterprises of all sizes, creating a data lake on a public cloud service is feasible. A cloud platform and data sources are all that are required to create a usable data lake. 

Improved customer relationship: Customer information from CRM platforms, social media analytics, buying history-containing marketing platforms, and incident tickets can all be combined in a data lake. 

Scalability: Data lakes are hosted in the cloud, just like AWS. They offer instant availability, scalability, and security. The volume of data that needs to be stored can be scaled by reputable data lake designing service providers. Profitability is increased because you only pay for what you use. Since all data is kept in one location, retrieving it for analytics is inexpensive. 

Level-Up R&D Innovations: A data lake can be used by your R&D teams to test hypotheses, define assumptions, and assess the outcomes. For instance, selecting the appropriate materials for product design can lead to quicker performance, doing genomic research can lead to more effective medications, and knowing how much customers are willing to pay for different attributes can help your teams determine how much to charge for their goods. 

How Continuuminnovations can help you?  

We continuuminnovations, Managed Cloud Service provides offers A to Z cloud solutions for many verticals. Our experienced cloud engineers recognize your needs and come up with the perfect solutions. You need not to go through the complex technical processes to build a Data Lake for your organization. We have good skill set with Data Lake solutions too. Reach out our experts and build your enterprise data lake without any hussles.