Data Lake

A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data. It is a place to store every type of data in its native format with no fixed limits on account size or file. It offers high data quantity to increase analytic performance and native integration.

Data Lake is like a large container which is very similar to real lake and rivers. Just like in a lake you have multiple tributaries coming in, a data lake has structured data, unstructured data, machine to machine, logs flowing through in real-time.

Challenges of

Building A Data Lake

In Data Lake, Data volume is higher, so the process must be more reliant on programmatic administration

It is difficult to deal with sparse, incomplete, volatile data

Wider scope of dataset and source needs larger data governance & support

Key Data Lake Updates

A Data Lake is a storage repository that can store large amount of structured, semi-structured, and unstructured data.

The main objective of building a data lake is to offer an unrefined view of data to data scientists.

Unified operations tier, Processing tier, Distillation tier and HDFS are important layers of Data Lake Architecture

Data Ingestion, Data storage, Data quality, Data Auditing, Data exploration, Data discover are some important components of Data Lake Architecture

Design of Data Lake should be driven by what is available instead of what is required.

Data Lake reduces long-term cost of ownership and allows economic storage of files

The biggest risk of data lakes is security and access control. Sometimes data can be placed into a lake without any oversight, as some of the data may have privacy and regulatory need.

Why Datanomist?

Provide connectors and integration adopters to automate the technical meta data capture from heterogenous systems

Automation of Data Quality Profiling, Monitoring and Implementation of tools using data lake

Enable Business Intelligence & Analytics on data quality statistics dashboard, and threshold, and trend monitoring

Led by Data Governance SMEs and Visionaries who have been involved with DG since inception and understand challenges, success areas and areas of improvement

Decades of real-word successes and lessons learnt to ensure the success of initiatives

Experience in best-of-breed implementation of tools to drive Business User adaption

Committed to be partners of our clients to ensure success which benefits the reputation of the Industry as a whole