You require an indestructible data platform for your modern applications. DataStax Enterprise (DSE) is built on Apache Cassandra™ to provide a data platform that can deliver 100% uptime and support today’s data demands.
Your applications require a multi-workload and multi-model data platform that can scale out. DSE provides elastic scale out capability with linear performance on a moment’s notice and manages massive data volumes in on-premises, hybrid, and multi-cloud environments.
Deliver instant contextual and personalized insights with an intelligent data platform that outclasses legacy, single workload databases designed decades ago.
Always evolving modern applications demand a future-proof data platform that can outlive today’s data and scalability requirements. With DataStax you can easily move new or existing data into DSE on a continual basis.
|Master-slave architecture||Masterless architecture|
|Moderate velocity data||High velocity data (time-series data from devices, sensors, etc.)|
|Data coming in from one/few locations||Data coming in from disperse geographic locations/many locations|
|Primarily structured data||Structured, with semi-unstructured|
|Always strongly consistent||Tunable consistency (eventual to strong)|
|Complex/nested transactions||Lightweight/simple transactions|
|Protect uptime via failover/log shipping||Protect uptime via distributed and fault-tolerant architecture|
|High availability||Continuous availability|
|Deploy app central location/one server||Deploy app everywhere/many servers/any cloud/microservices/serverless|
|Primarily write data in one location||Write data everywhere/anywhere|
|Primary concern: scale reads||Scale writes and reads|
|Scale up for more users/data||Scale out for more users/data|
|Maintain data volumes with purge||High data volumes; retain forever; horizontal scalability without boundaries|
|Transaction workloads||Mixed workloads of transactions and analytics|
|One data center or cloud region||Multi-data center, multi-cloud region, or hybrid cloud|
|Relational data||Multi-model (tabular, key/value, document, graph)|
Most IT analysts agree that NoSQL will dominate new software stack spending for years to come—but how do you know when to make the move away from legacy database software? In this whitepaper, you’ll learn the criteria to watch out for, and the keys to success when it comes time to migrate from a legacy relational database to a NoSQL platform built to support digital, mobile, and cloud applications that run everywhere.Learn More
DataStax Enterprise (DSE) is built on Apache Cassandra, the distributed database behind a vast array of applications that you likely engage with every day. The open source database powers apps for some of the world’s biggest brands that require zero downtime, high availability, and scaling to accommodate massive data volumes. Cassandra is ideal for applications that demand the highest data resiliency, like ecommerce or IoT apps, where uptime and scale—whether on premises or in the cloud—are essential. DSE provides a distributed, highly available, and fault-tolerant data management platform at scale, and with tunable consistency. Leading brands depend on DSE to power their business-critical applications, including Capital One, Cisco, Comcast, Condé Nast, Delta Airlines, eBay, Macy’s, McDonald’s, Safeway, and Sony. Developing on Cassandra is in demand with more than 40,000 job listings searching for engineers with Cassandra and NoSQL skills on LinkedIn. Given the high demands of today’s applications, we believe Cassandra has a bountiful future. But don’t just take our word for it. The 2019 Dice Tech Report found the rewards of learning Cassandra to be greater than others like MongoDB, MySQL, Postgres, Redis, and DynamoDB, and Cassandra placed among the top five technologies to know. Fast-Track Skills Acquisition: Model. Code. Go. Building your apps on Cassandra not only brings resilience for your data, but also the benefits of streamlining performance bottlenecks and simplifying the data management complexity that can come with developing apps for multiple legacy systems and data stores on mainframes or relational database management systems (RDBMS). Get started with Cassandra via DataStax Academy, where you can choose a learning path to plot your journey; learn how to develop, administer, or architect with DataStax and Cassandra; or specialize in graph, search, or analytics. Our Academy courses range from walk-throughs to hands-on coding lessons on topics like data modeling, advanced data types, connecting to drivers, replication and consistency, and the best CQL (Cassandra Query Language) or Gremlin commands for your queries. When you complete a DataStax Academy learning path, you’ll receive a Certificate of Completion—an accomplishment worth sharing on your LinkedIn profile! At DataStax Academy, you can also learn how to use DSE with Apache Kafka, Docker, and Kubernetes, as well as DataStax’s high-performance, pre-built connectors and production Docker images. You can also ramp up on DataStax graph technology for use cases that get you higher value from connected data in real time such as fraud detection, supply chain management, social network analysis, customer analytics, and recommendation engines. Connecting the Dots from Relational to NoSQL The big benefits in shifting from relational to Cassandra include a more elastically scalable and resilient data platform than relational databases and legacy technology—it minimizes time spent on downtime, transforming your data, and modeling complexity. “With a conventional database, we’d have to be really in the trenches, or completely re-architecting how we absorb that data. But because we had Apache Cassandra, we knew we’d have to add a few additional nodes to the cluster, at most, and without having to fundamentally re-architect our solution. That Apache Cassandra can scale with us with minimal hiccups is a great relief.” — Harry Robertson, tech lead at Ooyala One of the concerns that developers and data engineers often have when bridging the gap from familiar relational concepts like tables and SQL is relating them to NoSQL, CQL, and Cassandra models—but it’s easier than you think. Get Started Today Whether you’re deploying on premises or in the cloud, we’ve made things easier with a few simple steps that can help, and choosing DataStax Enterprise to accelerate your journey with Cassandra is a great place to start. We’ve also created a guide that covers all the comparisons, considerations, similarities, and key differences between Cassandra and relational databases:Getting Started with NoSQL and Apache Cassandra: Accelerating the Transition from Relational to NoSQL With our new training mantra, “model, code, and go,” tools, and simplified approach to getting started, it’s the right time to sign up to DataStax Academy, pick a learning track, start running DataStax distributed databases, and get certified. What are you waiting for? Model. Code. Go.Learn More
A DBA’s life is full of surprises – some more pleasant than others. As a DBA you’re pretty much “on it” 24-7-365: putting out fires, checking alerts, training people, keeping test databases in sync with production, debugging, and praying your developers are writing halfway decent code. If you’re swinging over to the NoSQL side (the “dark” side?), or considering it, then you’ll want to read this white paper, The DBA’s Guide to NoSQL. It’s essentially a NoSQL bible, sans orders from God -- or Darth Vader.Learn More
Many applications are built with relational data structures in mind. What happens when that structure gets out of control? In this talk we take you on a tour of migrating a travel application to a lightning-fast DataStax/Cassandra data architecture. We look at challenges of denormalizing transactional data, designing a UI for hundreds of fields, and how our data model evolved to keep us on track.Learn More
For years, organizations have relied on relational databases management systems (RDBMSs) to store, process, and analyze critical business information. The idea originated in a paper written in 1970 by a computer scientist named Edgar Codd, who thought to archive information in tables containing rows and columns. The concept was a major leap forward from the slow and inefficient flat file systems that businesses were using at the time, although these systems did work in conjunction with pre-relational model databases. The Rise of SQL Shortly after, IBM developed the SQL language to scan and manipulate sets of transactional data sets stored within RDBMSs. With SQL, it became possible to quickly access and modify large pools of records without having to create complex commands. SQL essentially enabled one-click access to sets of data. The idea took off, and the RDBMS eventually emerged as the most widely used data management system. Today, most organizations are still using RDBMSs one way or another. RDBMSs, however, have one major limitation: They are only capable of efficiently processing relatively small amounts of structured data—like names and ZIP codes. The NoSQL Imperative When the era of big data hit, a new kind of database was required. The real driver for NoSQL was the sheer shift in data volumes that the Internet brought. Prior to the internet, and in its early days, relational databases only had to deal with the data of a single company or organization. But when faced with the millions of Internet users that could discover a company's service in waves, the RDBMS model either broke or became very challenging to shard correctly. Relational databases also required a tremendous amount of maintenance. A database of a few thousand objects may handle things decently, but as you scale up, performance declines. This is a big problem—especially considering the massive volume of unstructured data that is being generated on a daily basis. According to 451 Research, 63% of enterprises and service providers today are managing storage capacities of at least 50 petabytes—and more than half of that data is unstructured. The concept of NoSQL has been around for decades. Believe it or not, businesses have been using non-relational databases to store and retrieve unstructured data since the 1960s. The technology, however, wasn’t referred to as NoSQL until developer Carlo Strozzi created the Strozzi NoSQL Open Source Relational Database in 1998. Strozzi’s database, though, was really just a relational database that didn’t have an SQL interface. It wasn’t until 2009 that we saw a true departure from the relational database model and the first working NoSQL application. NoSQL databases offer several advantages over relational databases. Most importantly, they can handle large volumes of big data. Other advantages include: Elastic scalability. Unlike relational databases, NoSQL databases can scale outward into new nodes instead of upward. This strategy is much more flexible, efficient and affordable than scaling with traditional legacy storage systems. Lower operating costs. One of the biggest downsides to using an RDBMS is the fact that you will have to deal with expensive servers. Since NoSQL databases leverage commodity server clusters, you can process and store larger data volumes at a lower cost. Reduced management. NoSQL databases are much easier to install and maintain as they are simpler and come with advanced auto-repair capabilities. While it’s not completely hands-off, NoSQL is much easier for network teams to manage on a daily basis. Bridging RDBMS With NoSQL Right now, NoSQL databases only account for about 3% of the $46 billion database market, but they are quickly gaining traction and on pace to become a legitimate long-term market disruptor. But while NoSQL is heating up and the RDBMS market is experiencing a significant slowdown, this doesn’t mean that businesses are running out and abandoning their RDBMS systems altogether. RBDMSs, after all, are still great at managing transactional workloads, which are heavily used today. The best solution often involves finding a way to use your legacy technology to support your new applications, and this means getting an enterprise data layer. What’s an enterprise data layer? It’s a way to connect your systems of record with your systems of engagement. Essentially, it’s a data management layer that precludes you from having to go through a painfully expensive and time-consuming “rip and replace” process, and it allows you to salvage your legay tech and put it to good use. You may still be stuck in the relational age, but that doesn’t mean you can’t take full advantage of the NoSQL revolution. The Architect’s Guide to NoSQL (white paper) READ NOWLearn More