Welcome to “IBM Db2 Warehouse.” After watching this video, you will be able
to: Describe key features of the IBM Db2 Warehouse. List IBM Db2 Warehouse use cases, and List key pipeline tool capabilities and integrations
with Db2 Warehouse. IBM Db2 Warehouse is a complete data warehouse
solution that offers a high level of control over your data and applications. Db2 Warehouse is easy to deploy within containerized
environments such as Docker. Db2 Warehouse is a highly flexible data warehouse
for client-managed, on-premises, cloud, and hybrid environments, that scales automatically with Massively Parallel
Processing, known as MPP, to support containerized deployments. Db2 Warehouse comes pre-packaged with access
to machine learning algorithms and utilizes in-database business analytics for speed. Db2 Warehouse enables you to automatically
generate data schemas, and seamlessly transform and load unstructured data sources into a
structured format for analysis. Db2 Warehouse speeds queries using BLU Acceleration,
which includes in-memory SQL columnar processing, data-skipping; and, as mentioned before, a
Massively Parallel Processor cluster architecture that speeds complex queries. Db2 Warehouse supports your AI analytics needs. Db2 Warehouse comes with dashboards for monitoring
performance and reporting issues. Some examples of included widgets are: Hardware and software issue counts, database alerts, and the amount of allotted storage used. You can also view a breakdown of how much
time is spent in different states, such as waiting for locks and time to execute SQL
queries, and a table of details regarding any database alert events. There are many other widgets available, such
as system and data server CPU utilization history, and others. Some of the use cases that Db2 Warehouse is
well-suited for include: Elasticity, or high-scalability requirements; Cloud, on-premises, or hybrid hosting; Consolidation and integration of disparate
data sources; Rapid development of line-of-business analytics
products, such as data marts; Management of sensitive or regulated data;
and Storage of older, colder structured SQL data. Db2 Warehouse supports a range of clients
and plugins, including: Java Database Connectivity, or JDBC, Node.JS, Spring, Python, R, Go, Spark, and Microsoft Visual Studio. IBM Db2 Warehouse, with its integrated Apache
Spark cluster, can be partitioned and deployed across a cluster of machines. You can submit Apache Spark jobs through stored
procedures to run against Db2 Warehouse, extending your analytical reach. You can use R Studio to analyze, wrangle,
model, and visualize your data with Db2 Warehouse. For example, you can create your own Docker
image that contains RStudio and all the packages and drivers you need to connect to Db2 Warehouse. You can even develop applications that run
R code, integrated with Db2 through a REST API. Db2 Warehouse also has a range of commonly
used open source drivers available on GitHub in the “IBM DB” repository. For example, under “popular repositories,”
you can find the “python-ibmdb” package, which provides a Python interface for connecting
to IBM DB2. In this video, you learned that: IBM Db2 Warehouse is a cloud-ready, highly
flexible data warehouse platform. Key features of IBM Db2 Warehouse include
speed, scalability, automated schema generation, and built-in machine learning. Use cases include data integration and rapid
development of data marts, and Db2 Warehouse integrates with JDBC, Apache
Spark, Python, and R Studio.