Table of contents
Scalability
Apache Cassandra is designed for linear scalability and can handle large volumes of data across many commodity servers without a single point of failure.
High Availability
Cassandra ensures high availability by replicating data across multiple nodes. Even if some nodes fail, the system remains operational.
Performance
It provides fast writes and reads by using a peer-to-peer architecture, making it highly suitable for applications requiring quick data access.
Flexible Data Model
Cassandra supports a flexible schema, allowing users to add new columns to a table at any time, making it adaptable for various use cases.
Geographical Distribution
Data can be distributed across multiple data centers, ensuring low-latency access for geographically distributed users.
No Single Point of Failure
Its decentralized nature ensures there is no single point of failure, which enhances resilience and fault-tolerance.
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Apache Cassandra is an excellent choice if you require a database system that can efficiently manage large-scale data while ensuring high availability and reliability. It is particularly well-suited for use cases that demand a robust, distributed, and scalable database solution.
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In fact, even in the absence of these commercial databases, users can effortlessly install PostgreSQL and leverage its built-in pgvector functionality for vector search. PostgreSQL stands as the benchmark in the realm of open-source databases, offering comprehensive support across various domains of database management. It excels in transaction processing (e.g., CockroachDB), online analytics (e.g., DuckDB),... - Source: dev.to / about 2 months ago
All messages are persisted durably for two minutes, but Pub/Sub channels can be configured to persist messages for longer periods of time using the persisted messages feature. Persisted messages are additionally written to Cassandra. Multiple copies of the message are stored in a quorum of globally-distributed Cassandra nodes. - Source: dev.to / 7 months ago
Cassandra is a highly scalable, distributed NoSQL database designed to handle large amounts of data across many commodity servers without a single point of failure. - Source: dev.to / 12 months ago
Distributed storage Distributed storage systems like Cassandra, DynamoDB, and Voldemort also use consistent hashing. In these systems, data is partitioned across many servers. Consistent hashing is used to map data to the servers that store the data. When new servers are added or removed, consistent hashing minimizes the amount of data that needs to be remapped to different servers. - Source: dev.to / about 1 year ago
On the other hand, NoSQL databases are non-relational databases. They store data in flexible, JSON-like documents, key-value pairs, or wide-column stores. Examples include MongoDB, Couchbase, and Cassandra. - Source: dev.to / about 1 year ago
HBase and Cassandra: Both cater to non-structured Big Data. Cassandra is geared towards scenarios requiring high availability with eventual consistency, while HBase offers strong consistency and is better suited for read-heavy applications where data consistency is paramount. - Source: dev.to / over 1 year ago
Dear r/python, we are happy to present you with our first open-source project. We have managed to implement a new driver for Python that works with Apache Cassandra, ScyllaDB and AWS Keyspaces. Source: over 1 year ago
NoSQL is a term that we have become very familiar with in recent times and it is used to describe a set of databases that don't make use of SQL when writing & composing queries. There are loads of different types of NoSQL databases ranging from key-value databases like the Reddis to document-oriented databases like MongoDB and Firestore to graph databases like Neo4J to multi-paradigm databases like FaunaDB and... - Source: dev.to / almost 2 years ago
To use NoSQL databases with code, you first need to choose a NoSQL database that suits your requirements. Some popular examples of NoSQL databases are MongoDB, Cassandra, Redis, and DynamoDB. Each of these databases has its own set of APIs and drivers that can be used to interact with them. Here, I'll use MongoDB as an example and explain how to perform CRUD operations using Python and its PyMongo package. - Source: dev.to / about 2 years ago
Change Data Capture is a technique used to capture and propagate changes made to a database such as MySQL, Microsoft SQL, Oracle, PostgreSQL, MongoDB, or Cassandra. CDC works by continuously monitoring the database for any changes made to the data. Multiple types of change data capture patterns can be used for data processing from a database. These include log-based CDC, trigger-based CDC, CDC based on timestamps,... - Source: dev.to / about 2 years ago
The popular NoSQL database Cassandra utilizes a compression algorithm called LZ4 to reduce the footprint of data at rest. LZ4 is characterized by very fast compression speed at the cost of a higher compression ratio. This is a design choice that allows Cassandra to maintain high write throughput while also benefiting from compression in some capacity. - Source: dev.to / over 2 years ago
Java is an easy language regarding syntax and language constructs. The complexity arises from the problems that you are solving with the language - for example, many databases are written in JAVA (for example the https://cassandra.apache.org/_/index.html). The core of the database is very complex, thankfully Java is easy to read so once you are quite familiar with the language, you can focus on solving interesting... Source: over 2 years ago
There are several alternatives to Redis that are worth considering, depending on your specific needs and requirements. Some popular options include Memcached, which is another in-memory data store that is often used for caching, and Apache Cassandra, which is a distributed NoSQL database that is designed for scalability and high availability. - Source: dev.to / over 2 years ago
In general, there are two different approaches, one is to choose a common NoSQL database, such as MongoDB, ElasticSearch or Apache Cassandra. All of these NoSQL databases have good scalability and can support complex queries. In addition, these databases are very mature, so the learning curve is low for both use and operation. - Source: dev.to / over 2 years ago
Next, review deployment complexity such as DB-less versus database-backed deployments. For example, Kong does require running Cassandra or Postgres. Apigee requires Cassandra, Zookeeper, and Postgres to run, while other solutions like Express Gateway and Tyk only require Redis. Apache APISIX uses etcd as its data store, it stores and manages routing-related and plugin-related configurations in etcd in the Data... - Source: dev.to / over 2 years ago
Database - Considering the traffic and scale of the service we can go with a non-relational database(MongoDB, Apache Cassandra, etc..) or a relational database(PostgreSQL, MySQL, etc...) and Redis for in-memory caching. - Source: dev.to / over 2 years ago
If you're planning on querying the db using the filenames, a NoSQL option might be Cassandra (https://cassandra.apache.org/_/index.html). I haven't used it personally but I've heard it can manage large amounts of data efficiently and pretty fast. Source: over 2 years ago
For instance, Apache Cassandra, the open source NoSQL database that was introduced by Facebook in 2007, is both horizontally scalable and data center aware. If we were to deploy Cassandra to solve this problem, it would look something like the image below. - Source: dev.to / over 3 years ago
Since the data is not strongly relational, NoSQL databases such as Amazon DynamoDB, Apache Cassandra, or MongoDB will be a better choice here, if we do decide to use an SQL database then we can use something like Azure SQL Database or Amazon RDS. - Source: dev.to / almost 3 years ago
We will split the data between different services each having ownership over a particular table. Then we can use a relational database such as PostgreSQL or a distributed NoSQL database such as Apache Cassandra for our use case. - Source: dev.to / almost 3 years ago
Maybe rare for someone to be seeking their first coding job at that age. But plenty of us are in our 50s or older and still coding up a storm. And not necessarily ancient tech or anything. My current project exposes analytics data from Apache Druid and Cassandra via Go microservices hosted in K8s. Source: over 2 years ago
Apache Cassandra, a highly scalable, distributed NoSQL database, has garnered substantial attention across various domains primarily for its ability to handle large volumes of structured data without a single point of failure. It often emerges as a prominent choice for enterprises dealing with IoT applications, fraud detection, recommendation engines, and messaging applications where real-time insights and swift data processing are paramount.
Scalability and High Availability
A key strength of Apache Cassandra is its architectural design, which ensures scalability and high availability. This database is engineered to operate seamlessly across multiple data centers, affording users assurance that their data is not only distributed but also replicated in diverse geographical locations. This replication capability makes it particularly appealing for applications that demand high availability and resilience against data center failures.
Wide Column Store
Cassandra's storage model, known as the wide-column store, enables it to manage both structured and semi-structured data effectively. This flexibility suits organizations that require adaptable data models or seek to integrate structured SQL-like capabilities within a NoSQL framework, thereby addressing a wide array of application requirements smoothly.
Integration Capabilities and Ecosystem
The ecosystem surrounding Cassandra is robust, featuring tooling support for integration with languages like Java. Its architecture also enables integration with technologies like ScyllaDB and AWS Keyspaces, thanks to compatible drivers, thereby broadening its use case flexibility. Furthermore, articles have highlighted how Apache Cassandra efficiently manages data compression using LZ4, maintaining high write throughput, which is vital for applications requiring fast reads and writes.
Consistent Hashing and Distributed Storage
Cassandra employs consistent hashing to partition and distribute data evenly across nodes, an indispensable feature for distributed storage systems. This minimizes data movement when nodes are added or removed, thus providing seamless scalability.
Comparative Position in the Market
Cassandra is often juxtaposed with competitors such as MongoDB, Redis, and HBase. Compared to HBase, for instance, Cassandra prioritizes high availability and eventual consistency, making it a better fit for applications where fault tolerance and uptime are critical, albeit at the expense of immediate consistency. Against MongoDB, another NoSQL giant, Cassandra is often recognized for its superior write capabilities and its strategic advantage in multi-data-center deployment.
Emerging Use Cases and Technological adaptability
Recent discussions emphasize Cassandra's capabilities in the realm of vector search functionality, along with PostgreSQL and other databases. Such adaptability indicates Cassandra’s expansion beyond traditional big data use cases into new technological domains like AI and machine learning. Moreover, the public eye has acknowledged Cassandra's role in complex system designs where non-relational data modeling and distributed architecture are essential.
In summation, Apache Cassandra is lauded for its capabilities in handling massive data workloads while ensuring high availability and resilience. The database's adaptability across industries exemplifies its enduring relevance and commercial viability in a rapidly evolving technological landscape.
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