VantageCloud Lake supports the Time Travel feature, i.e. the ability to access the data in a table as it looked at a previous moment. This post explains how.
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: Autoscaling the Compute Clusters
This post explains how the autoscale mechanism works for the VantageCloud Lake Compute Clusters and what triggers it.
Considerations To Load Data Into VantageCloud Lake
Teradata VantageCloud Lake architecture uses two file systems: Block File System (BFS) and Object File System (OFS). While OFS is cost-effective, BFS enables faster operations and features not available in OFS, such as temporal tables and row-level security. Additionally, the Lake instances are in the UTC zone, which conditions how to load data. This post explains the primary considerations for placing data in Lake.
VantageCloud Lake Architecture
VantageCloud Lake is Teradata’s cloud-native data and analytics platform. This post explains Lake’s architecture, including OFS and BFS storage, and its main capabilities.
Teradata Enterprise: Ins and Outs of the Encryption Keys
This post explains the options Teradata supports for using encryption keys in VantageCloud Enterprise and some recommendations.
Teradata VaaS on AWS: Network configuration
Cheat sheet with the key network elements you need to connect with your Teradata VaaS on AWS and a detailed explanation.