Cheat sheet with the key network elements you need to connect with your Teradata VantageCloud Lake on Azure and a detailed explanation.
Category: Relational Databases
A Relational Database consists of a set of logically related tables.
A table is a two-dimensional representation of data consisting of rows and
columns.
A row is one instance of all the columns of a table. E.g., the information about a single employee is in one row, such as full name, ID number, address, etc.
The sequence of the rows in a table is arbitrary.
We design Relational Databases to protect access to data and retain its value and integrity.
The key idea about Relational Databases is that they permit associations by data value across more than one table.
Relational Databases do not use access paths to locate data. Instead, data connections are made through data values. In other words, the database software makes data connections by matching values in one column with the values in a corresponding column in another table. We call this connection JOIN.
As we have already said, a Relational Database is a collection of relational tables. The database collections are stored in a single installation of a Relational Database Management System (RDBMS). The words “management system” indicate that not only is this a relational database, but also there is underlying software to provide additional functions. These features include transaction integrity, security, relational operations (table scans, index scans, projections, selections, joins, aggregations), etc.
Many vendors in the market build RDBMS. While all of them allow establishing relationships among the data in different tables, there are critical differences in their internal architecture. Depending on your use case and requirements, you may choose one RDBMS or another.
In this category, you will find posts that refer to use cases, best practices and know-how instructions on Relational Databases from different vendors.
VantageCloud Lake on AWS: Network configuration
Cheat sheet with the key network elements you need to connect with your Teradata VantageCloud Lake on AWS and a detailed explanation.
Flow in VantageCloud Lake: From Bust to Boom in Data Ingestion to Insights
VantageCloud Lake service that allows data users to upload external files into Lake quickly and easily.
Flow paired with the Visualization features added to the Lake Console democratises getting quick insights into any information. See the below below for a demo.
VantageCloud Lake in a Nutshell
Post that includes an infographic summarising the critical VantageCloud Lake elements and the basis for using them for a quick start.
The Path of a Query in VantageCloud Lake
This post explains the path a query takes to move through VantageCloud Lake and what steps execute on every cluster.
Session Manager in Lake: The Key to High Availability
The Session Manager routes requests from the client application to the Primary Cluster in VantageCloud Lake. Teradata designed the Session Manager to provide users with an additional high-availability layer. The reason is that the Session Manager minimises the impact of planned and unplanned outages on the workload running on the Lake instances. This post explains how the Session Manager works, how it behaves with intended and unplanned outages, and the Blue-Green upgrades.