Data Lake
Data Lake
A data lake is a repository of data stored in its natural or raw format. It is usually a single store of data that includes raw copies of source system data, sensor data, social data, converted data, and more. It is used for tasks such as reporting, advanced analytics, and machine learning.
A data lake can include structured data, semi-structured data, unstructured data, and binary data. A data lake can be established on-premises or in the cloud.
Capability
- Data movement:Our Lake Houses allow real-time importation of both any amount and any type of data. Data is collected from multiple sources and moved into the Lake House in its original format. This process allows one to scale data of any size, while saving time for defining data structures, schema, and transformations
- Securely store and catalogue data: Our Lake Houses allow storing of relational and non-relational data. Relational data includes data like operational databases and data from line of business application. Non-relational data includes operations/maintenance logs, mobile apps, and IoT devices. The Lake Houses also help understand the nature of data in the lake through crawling, cataloguing, and indexing of data. Finally, data must be secured to ensure your data assets are protected .... be it in the cloud, on-premise, or hybrid
- Analytics: Our structured Lake Houses allow data scientists, data developers, and business analysts to access data with their choice of analytic tools and frameworks. This includes open-source frameworks such as Apache Hadoop, Presto, and Apache Spark; and commercial offerings from data warehouse and business intelligence vendors. Lake Houses allow you to run analytics without the inconvenience of having to move your data to a separate analytics system
- Machine Learning: Our Lake Houses allow customers to generate different types of beneficial, actionable insights. These include reports based on historical data, performing machine learning with pre-built models, forecasting likely outcomes, and suggesting a range of prescribed actions to achieve optimal results
Value Proposition
- Monetization of data by performing analytics and taking decisions
- Minimization of cost by keeping properly structured and good data, and by not retaining unnecessary data
- Rectification of centralized data, which brings useful data points and helps in prediction accuracy
