Reinventing near-real-time digital & content analytics with data virtualization

A logical Data Warehouse is a component of Data Management architecture wherein it is the topmost layer of the Traditional Data Warehouse. It allows you to view data without moment or transformation. There are four major elements of Logical Data Warehouse, which are The Central Database, ETL tools, Metadata, and Access Tool.

AWS is the abbreviation used for Amazon Warehouse Services and S3 stands for Simple Storage Service. AWS offers simple storage services for the scalability of the e-commerce network.

Blob means Binary large Object, which is a set of binary databases cataloged as single entries. It can be stored in the form of images, audio, videos, or other multimedia objects.

Spark is an open-source general-purpose programming framework that helps in the distribution of Data Processing Engine by focusing on machine learning, interactive query, and real-time workloads. It does not own a storage system so it relies on other sources such as Amazon S3, Amazon Redshift, HDFS, etc.

Python is the easiest and simplest programming language that is interactive, interpretive, and object-oriented. It is very dynamic. And saves a lot of time while coding.

Google Collab notebooks are the form of Jypter notebooks that run in the cloud and these are integrated with Google Drive. These are helpful in writing, storing, and arbitrary python codes.

Automation of Query Execution means detaching human access or interaction with SQL queries, retrieving that to some distinctive folder, and executing the distribution process to the end-user.

Data access is the process of providing access to the end-user for retrieving the data from the repositories or database. DAR is the term popularly used that stands for Data Access Right, which means the request for permission to access, retrieve, store and manipulate the data.

Disparate source or disparate data source is consist of disintegrated and low-quality data. The integration of several disparate data sources is combined and used as an integrated source of data.

Data Visualization is needed by enterprises because it instantly combines a large set of data from distinct sources, helps in the maintenance of the Data Warehouse, saves time, improves productivity, and eliminates latency.

It is a technique used by the computer system for storing temporary data in the RAM (Random Access Memory) for encouraging the quick recovery of data.

Lyftrondata offers you to choose your most valuable data and pulls it from all your connected data sources in just a few clicks. It aims at Lyft, Shift, and loads any type of data instantly on Snowflake.

Lyftrondata's Data Virtualization eliminates the complexities by simplifying the velocity by automating the pipeline creation, reducing the cost, and fastening the speed to deliver real-time digital data and content analytics. Lyftrondata offers density for making quicker business decisions, timely data exchanges between enterprises and much more.