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Enterprise Data Lake Architectures & Implementation Solutions
Store your structured and unstructured data at any scale in a centralized repository with our data lake Implementation solutions.
understanding why data lake is needed
With larger chunks of data collected in enterprise data lake from various sources, the need to leverage information and help make better-informed business decisions arises. Today, companies recognize the importance of migration to the cloud and the ease at which it increases data efficiency.
Using Big Data technologies, Datametica assists firms by setting up a Data Lake which holds structured and unstructured data securely. With our unique automation techniques, the process is accomplished at a faster pace and reduced cost.
exploring how data lake functions
Using recurrent processes throughout the data pipeline, Datametica ensures successful data lake Implementation. This is possible, from data lake assessment to data lake analytics, with the help of our automation tools.
benefits of data lake implementations
Transforming Big Data into powerful business solutions
Collating all ecosystems into a single point of truth on Cloud and on-premise with the help of reference data lake architecture.
Achieving faster delivery at lower costs by using the Extensive Accelerator Repository. Time & effort taken reduces by 50%.
By creating a security framework, the data is protected (in transit and at rest), authenticated, provided with authorization, and goes through an auditing process.
Migrate data to a centralized repository with ease and automate future ingestions from any source.
With the eCat tool, data is consistently defined across the enterprise by fulfilling major gaps and missing functionalities. It also helps in automatically capturing metadata for data quality and governance
FAQs
What is a data lake?
A data lake is a central storage repository that holds big data from many sources in a raw, granular format. It can store structured, semi-structured, or unstructured data, which means data can be kept in a more flexible format for future use.
Difference between data lake and datawarehouse?
Data Lake are generally used to store structured, unstructured data from various sources whereas Data Warehouse is used to store clean and structured data which caters specific business needs.
To know more about the detailed comparison between data lake and data warehouse read our blog here.
