Course: Working with Apache Cassandra
duration: 11 hours |
Language: English (US) |
access duration: 90 days |
Details
Apache Cassandra is a decentralized, distributed, wide-column store that provides great performance at petabyte-scale data for specific types of data and operations. Cassandra is great for data that can be accessed via unique keys and where each row has potentially very different column attributes. In this course, you’ll learn how to use all the features Cassandra offers. Start by exploring how to enumerate the defining attributes of Apache Cassandra and identify when to use and not use Cassandra. Next, you’ll learn to configure tunable consistency in Cassandra, configure different replication strategies, and create and use tables. Move on to partitioning and clustering keys to identify types of queries that are and are not allowed in Cassandra. Finally, you’ll learn to create indexes on Cassandra tables, perform grouping and aggregation operations, leverage UDFs, and work programmatically with Cassandra from a Java client.
Result
After completing this course you’ll be able to use Apache Cassandra in your daily work. You’ll know how to use the features, replication, consistency, CQL, collection, user-defined field, portioning, clustering keys, indexes, aggregations and programmatic access.
Prerequisites
No formal prerequisites. However, some prior knowledge of the topic is highly recommended.
Target audience
Database Administrators
Content
Working with Apache Cassandra
Working with Apache Cassandra: Getting Started
Apache Cassandra is a decentralized, distributed, wide-column
- store that provides great performance at petabyte-scale data for
- specific types of data and operations. Cassandra is great for data
- that can be accessed via unique keys and where each row has
- potentially very different column attributes. In this course, learn
- how to install Cassandra and make it available for use with Docker.
- Next, discover how to create Cassandra clusters and inspect them
- using the nodetool utility. Finally, explore how to leverage a
- datacenter in Cassandra, correctly use the snitch, and set the
- snitch used to the GossipingPropertyFileSnitch. Upon completion,
- you'll be able to enumerate the defining attributes of Apache
- Cassandra and identify when to use and not use Cassandra.
Working with Apache Cassandra: Using Replication, Consistency, & CQL
Apache Cassandra provides partition tolerance via its
- decentralized design and allows for configuration between either
- consistency and availability. This is why Cassandra is said to
- support tunable consistency. In this course, learn how Cassandra
- organizes tables into keyspaces and how to specify the replication
- strategy and factor at the keyspace level. Next, practice
- configuring various read and write consistency levels and explore
- the trade-offs between consistency and availability. Finally,
- discover how to run various CQL queries to create tables, insert or
- update data, and query data. Upon completion, you'll be able to
- configure tunable consistency in Cassandra, configure different
- replication strategies, and create and use tables.
Working with Apache Cassandra: Using Collection & User-defined Fields
Apache Cassandra does not support joins, which means that data
- is inherently denormalized. That gives rise to the need for
- collection fields such as sets, maps, and lists, as well as for
- user-defined types that allow the table creator to encapsulate
- related fields. Begin this course by working with set, map, and
- list types. Then, focus on user-defined types and counter fields.
- Finally, you’ll learn how to work with CSV and JSON data -
- including reading data from and writing data to a CSV file and
- displaying query results in JSON format. Upon completion, you'll be
- able to enumerate and contrast collection fields in Cassandra;
- define and use set, map, and list types; leverage user-defined
- types and counters; and work with JSON and CSV data and the COPY
- command.
Working with Apache Cassandra: Partitioning & Clustering Keys
Primary keys play a special role in Apache Cassandra. Not only
- are they used to uniquely identify a row in a table, they are also
- used to decide where and how data is stored in the underlying
- cluster. Begin by creating tables with different combinations of
- partitioning and clustering key columns, querying the tables, and
- confirming the keys were taking effect. Then, explore the exact
- semantics of queries on partition and clustering key columns.
- Finally, learn how to use the nodetool and grep utilities to view
- properties of partitions and to verify how rows are mapped to
- partitions on the basis of token ranges assigned to each partition.
- Upon completion, you will be able to contrast primary keys in
- Cassandra with those in other data technologies, differentiate
- between clustering and partition keys, and identify types of
- queries that are and are not allowed in Cassandra.
Working with Apache Cassandra: Indexes, Aggregations, & Programmatic Access
Apache Cassandra is a distributed NoSQL technology meant for
- large-scale data, so programmatic access to Cassandra is especially
- important. Cassandra supports client libraries in several major
- programming languages, like Java, Python, and C#. Developers use
- these to connect to Cassandra and to work with it from code. Begin
- by creating and using indexes in Cassandra. Then, define and invoke
- user-defined functions (UDFs) to perform aggregations. Finally,
- you’ll create a Java Maven project with the datastax library as a
- dependency and connect to a Cassandra database using that library.
- You will create a Cassandra session, execute various operations
- using the datastax APIs, and confirm that these queries went
- through successfully. Upon completion, you will be able to create
- indexes on Cassandra tables, perform grouping and aggregation
- operations, leverage UDFs, and work programmatically with Cassandra
- from a Java client.
Course options
We offer several optional training products to enhance your learning experience. If you are planning to use our training course in preperation for an official exam then whe highly recommend using these optional training products to ensure an optimal learning experience. Sometimes there is only a practice exam or/and practice lab available.
Optional practice exam (trial exam)
To supplement this training course you may add a special practice exam. This practice exam comprises a number of trial exams which are very similar to the real exam, both in terms of form and content. This is the ultimate way to test whether you are ready for the exam.
Optional practice lab
To supplement this training course you may add a special practice lab. You perform the tasks on real hardware and/or software applicable to your Lab. The labs are fully hosted in our cloud. The only thing you need to use our practice labs is a web browser. In the LiveLab environment you will find exercises which you can start immediately. The lab enviromentconsist of complete networks containing for example, clients, servers,etc. This is the ultimate way to gain extensive hands-on experience.
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