Apache Cassandra is a NoSQL, Open-Source Column-Store Database that started at Facebook. It has identical data nodes clustered together to remove single failure points and bottlenecks, ensuring data safety. Cassandra has a peer-to-peer data distribution model instead of a master-slave replication model. The NoSQL Database delivers fast read-write performance, always-on availability, effortless replication, and unlimited linear scalability. Cassandra can handle thousands of concurrent operations per second and Petabytes of information, allowing organizations to manage high data volumes across multi-cloud and hybrid environments.
In this blog, you’ll learn about Apache Cassandra, Data Modeling, and top Apache Cassandra Modeling tools.
Table of Contents
Basic understanding of Data Storage Systems.
What is Apache Cassandra?
Apache Cassandra is a fault-tolerant, highly available, scalable, and distributed Database designed to handle data across multiple commodity servers with no single point of failure. It is a Column-Oriented Database, and its distribution is based on Amazon Dynamo and Google’s BigTable. Cassandra was created at Facebook and differs from Relational Database Management Systems. A NoSQL or Not Only SQL Database offers a mechanism to retrieve and store data in tabular relations.
Key Features of Apache Cassandra
- Fault-Tolerant: Apache Cassandra treats all the nodes equally. If one node goes down, it won’t affect the entire system. Cassandra replicates the data to ensure that it can easily and quickly replace it if any node fails.
- Elastic Scalability: You can easily add nodes to the Cassandra cluster at any given time as your needs grow. Cassandra grows horizontally rather than vertically, which is best for organizations with offices across various geographical areas or companies that want to scale and open new offices.
- Flexible Data Storage: It can accommodate all data formats, including structured, unstructured, and semi-structured. It can also dynamically adjust any changes to the data structure.
- Cassandra Query Language: Standard Query Language or SQL is used for Relational Databases and suits businesses that intend to scale vertically. It deals with fixed schemas for a moderate volume of data and has a table-based structure. However, Cassandra uses a NoSQL language to move data horizontally across clusters and is not confined to fixed schemas.
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What is a Data Modeling Tool?
Data Modeling is the visualization of information to show connections between data. It helps to illustrate the data stored within the system and the relationships between these data. It is a predictable method of managing and defining data across organizations.
Types of Data Modeling
- Hierarchical Data Models: Data is organized into a tree-like structure and has a single root to which data is attached. The hierarchy begins from the root and expands like a tree.
- Relational Data Models: The data and their relationships are represented through interrelated tables where the column represents the entity’s attribute, and the rows are used to represent records.
- Network Data Models: A graphical representation of objects and the relationship among objects.
- Entity-Relationship Model: A Blueprint or an ER Diagram of a Database used to implement the Database.
Now that you’re familiar with Data Modeling, let’s focus on Cassandra Modeling tools.
Components of Cassandra Modeling Tools
Data Modeling in Cassandra is query-driven, and it is the process of identifying entities and their relationship. Following are the components of a Apache Cassandra Modeling tool:
The Data Model in Cassandra contains Keyspaces at the most basic level. Keyspaces are data containers comparable to RDBMS Databases or schemas. Keyspaces include tables, but they aren’t predefined. Users have to establish Keyspaces before creating tables.
- Replication Factor: It’s the number of machines in the cluster that receive copies of the data.
- Replica Placement Strategy: Replication of 3 determines that each data row in the Keyspace will have 3 copies. There are strategies like rack-aware strategy, old network topology strategy, and data-center-shared strategy to replicate data through Keyspaces.
- Column Families: A Keyspace is a container with a list of one or more column families. A column family contains a collection of rows. These column families represent your data structure.
Tables have data in horizontal and vertical forms, and they also have a primary key. Keyspaces can include unlimited tables, but a table can only belong to one Keyspace. Keyspaces have one too many connections.
Columns in the Cassandra Modeling tool define a table’s data structure. Each column is connected to a data type, such as text, integer, double, or boolean.
The Database in Cassandra Modeling tool is distributed over various machines that operate together. The end of the outermost container is called the cluster of the Database. Every cluster node contains a replica, which helps in failure handling in case one of the nodes fails.
Rules of Cassandra Data Modeling
Things you shouldn’t do while Data Modeling with Cassandra Modeling tools:
- Don’t Minimize the Number of Writes: In Cassandra, the writes aren’t free but are comparatively cheap. The platform is optimized for write throughput, and every write is efficient. However, you should always use extra writes to improve performance and efficiency.
- Don’t Minimize Data Duplication: Data Duplication in Cassandra is necessary in case of node failure. You will have to duplicate data to get the most efficient reads, so don’t try to minimize Data Duplication. Besides, disk space is the cheapest resource in Cassandra.
Types of Data Models in Apache Cassandra
The Cassandra Database assigns data to nodes in the outermost range of a ring cluster: the Keyspace is where each node contains a replica to protect data during failure. Apache Cassandra organizes data based on specific queries and follows a query-driven approach. The Database is based on fast reads and writes requirements, so retrieval speed improves with schema design. Queries start by selecting data from tables; query patterns define user phrases, and schema defines how table data is arranged.
There are 3 types of Data Models in Apache Cassandra:
Conceptual Data Model
It’s an abstract view of your domain and is technology-independent. A Conceptual Data Model in Cassandra is not specific to any Database system. The purpose of the Conceptual Data Model is to define essential objects, understand data, and define constraints for modeling.
Logical Data Model
The Logical Data Model defines the attributes, fields, and columns with functionality. It also establishes the partition key, which is vital in executing queries in Cassandra. The partition key defined at this stage is helpful during indexing and performing CQL queries.
Physical Data Model
The Physical Data Model in the Cassandra Modeling tool describes table queries to build tables and Data Models. You will assign data types and analyze models by performing size calculations.
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Top Apache Cassandra Modeling Tools
Let’s discuss the top 2 Apache Cassandra Modeling tools.
Hackolade supports schema design for Apache Cassandra and various other NoSQL Databases. It’s an open-source Cassandra Data Modeling tool that offers visual Data Modeling and an easy-to-use interface for creating models. There are 2 ways of modeling Databases in Cassandra with Hackolade: Forward-Engineering and Reverse-Engineering. In Forward Engineering, users must develop sample documents with models to visualize the represented data. On the other hand, Reverse Engineering uses metadata to recollect the data. You can either submit XSD from Erwin, JSON documents, or PowerDesigner. Users can publish data in tables and diagrams with Hackolade.
You can try the Hackolade Cassandra Modeling tool free of cost for 14 days or subscribe to a monthly subscription of €125 per month or an annual subscription of €1250 per year or contact the company for a concurrent subscription.
DataStax Enterprise is built on the foundation of Apache Cassandra and adds operational reliability, monitoring, and security layer. It offers streamlined development with which users can seamlessly build apps for distributed data sources and create mixed models with Kafka and docker integration, DSE tools, and more. This Cassandra Modeling tool is optimized for low latency and high throughput. It offers advanced replications, fast bulk loader, and analytical queries.
DataStax Enterprise is fully integrated with Search, Analytics, and Graph. It also meets data security compliance requirements and offers end-to-end encryption, access control, and data auditing. DataStax Enterprise comes with zero downtime, zero lock-ins, and Data APIs.
Users can try DataStax for free. However, it has 2 paid models: pay as you go and enterprise. In the first pricing plan, users only have to pay for services they use and can quickly scale as their application grows. On the other hand, the Enterprise plan has volume discounts with annual commitments and health checks for optimizing performance.
In this blog, you learned about Apache Cassandra, Data Modeling, and the top 2 Apache Cassandra Data Modeling tools. Apache Cassandra is scalable, durable, and allows the addition of new machines without downtime. Since Cassandra doesn’t rely on master-slave architecture, users can easily process and redirect writes to any available nodes without closing down the system.
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Share your experience of understanding the Cassandra Modeling tool in the comments section below.