Data modeling is the process of creating relationships and rules about objects, so that we can decide how to store them in a database. Data modeling defines how we store and query our database systems.   Today’s episode features a discussion of data modeling in Cassandra with Jon Haddad, an evangelist at Datastax. The distributed

Apache Cassandra is a distributed database that can handle large amounts of data with no single point of failure. Since 2008, Cassandra has been widely adopted and the software and the community around it have grown steadily. A software developer interacting with Cassandra uses CQL, the Cassandra Query Language. ScyllaDB is another open-source database that

Ten years ago, databases were much simpler. Most companies would only have one or two types of databases in production. Today, the age of one-size-fits-all is over. Companies have multiple databases to deal with different types of use cases, and databases have become distributed to multiple nodes in order to be scalable. Ozgun Erdogan of

The process of building a software project requires us to make so many architectural decisions. Which programming languages should be used? Which cloud service provider? Which database? A newer type of building block is the analytics platform. Companies need to track events, aggregate metrics, and change the user’s experience based on aggregated data. Dan Kador

At Netflix, 500 billion events and 1.3 petabytes of data are ingested by the system per day.  This includes video viewing activities, error logs, and performance events. On today’s episode, we dive deep into the data pipeline of Netflix, and how it evolved from their 1.0 version to the modern 2.0 version. Before listening to

Distributed databases are difficult to operate, and Crate.io wants to change that. Crate is a fast, scalable, easy-to-use SQL database that is built to run in containerized environments. An average software company runs several databases–MySQL for relational store, MongoDB for a document database, HDFS for blob storage and data warehouse, elastic search for search. On

Microsoft has built a suite of technologies on top of its Azure infrastructure as a service. Today, we discuss Azure Stream Analytics, a real-time event processing engine developed at Microsoft. Azure Streaming allows for constant querying of incoming data streams, and my guest Santosh Balasubramanian discusses Azure and the movement from batch processing to stream

Apache Spark is a framework for fast, distributed, in-memory analysis. Apache Cassandra is a distributed database management system that provides high availability and fast throughput. Today, we are collecting fast, big data streams from user behavior, smart phones and sensors, and the disk checkpointing of and query language of Hadoop MapReduce is no longer adequate.

Apache Kafka has become the most popular open-source solution for persistent replicated messaging in the Hadoop ecosystem. But some software engineers who are working with “big data” don’t want to deal with the configuration and set up of Kafka. One way to side step this problem is to go with a managed solution, like Microsoft

“Eventual consistency is really kind of a marketing term from some of these NoSQL systems – it’s not really consistent in any strong sense of the term.” Google has published papers on distributed systems such as BigTable, Chubby, and the Google File System. During this episode, we focus on a product that takes inspiration from