Business Platform Team

Anant Corporation Blog: Our research, knowledge, thoughts, and recommendations about building and managing online business platforms.

Tag Archives: datastax


What is DataStax?

DataStax is a database platform, that uses Apache Cassandra, which is built for the performance and availability demands of Web, Mobile, and IoT applications. It gives enterprises a secure always-on database that remains operationally simple when scaled in a single datacenter or across multiple data centers and clouds.

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Anant is speaking at DataStax Accelerate 2019

The biggest Apache Cassandra event in a couple of years, DataStax Accelerate, is happening in National Harbor, MD from May 21 to 23.

We’re excited to have been picked as presenters for one of the most significant big data conferences in the world.

Every customer interaction including purchases offer redemptions, web/mobile interactions, and emails, generates massive amounts of customer data, challenging the boundaries of traditional data management systems.

In this session, we’ll cover how implementing a real-time marketing data platform helped a global Quick Serve Restaurant (QSR) understand important customer signals, including behaviors, needs, and style to generate actionable insights for various applications in real-time.

We’ll provide an overview of the solution built with DataStax Enterprise covering the different platform components including storage, stream processing, and data APIs, and how the components can be configured for various use cases.

Hope to see you at the conference as well as the talk (50% off with the code Singh50)! If you have interest in DataStax consulting, managed services, or remote DBAs feel free to reach out to us at solutions@anant.us or pick a time to chat.

We’ve created the definitive resource for Apache Cassandra over at Cassandra.link, contributions, as well as feedback, are welcome!

Analytics

Delivering Real Time Analytics with Sitecore and DataStax

In one of my earlier articles, I talked about why your company or organization should adopt Sitecore as your Experience Platform. It’s a good platform for users, content authors, and developers to create compelling and engaging digital experiences as well as collect information on website traffic. Machine learning and analytics in personalized content are two of the most compelling features of Sitecore. In today’s world, companies, particularly the Fortune 500, require real-time analytics to help drive stakeholder goals.

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Monitoring Complex Systems

Resources for Monitoring Datastax, Cassandra, Spark, & Solr Performance

This resource for monitoring Datastax, Cassandra, Spark, & Solr performance is just the first iteration of a longer initiative to create the best knowledge base on these real-time data platform technologies such as DataStax Enterprise (Cassandra, Spark, and Solr) as well as for Kafka, Docker, and Kubernetes. Our firm, Anant, has been working with Solr/Lucene for several years, and then over the years picked up Spark and Cassandra, and then made the logical move to become experts at and partners with Datastax.

Datastax OpsCenter is good but we’re wise enough to say, however, that it is just the beginning of the toolset needed to really understand what is happening under the hood in the component technologies that comprise of the Datastax Enterprise Platform. When monitoring to scale complex systems such as business platforms you need to review all signals, not just those that come from the database.

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Datastax / Cassandra Data Modeling Strategies

Common Problems in Cassandra Data Models

In our opinion Cassandra is one of best nosql database technologies we’ve used for high availability, large scale, and high speed business platforms, More specifically, we work with Datastax Enterprise version for Cassandra where the clients are above a certain size and need to have enterprise grade support 24/7 365 days a year with expertise around the world. There are many topics in which I could have written about as my first “Cassandra” post on our blog, but decided to write about what I call the three stooges of Cassandra data modeling: Larry (Tombstones), Curly (Data Skew), and Moe (Wide Partitions).

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