The popular nosql database Cassandra eschews a master/slave partitioning design in favor of a fully peer-to-peer model which makes it robust and scalable but adds significant complexity to some areas of its design. I will explain the challenges and successes Cassandra has had in creating lightweight transactions, materialized views, and strongly consistent cluster membership within … Read More
I had recently come off of a three year contract and found myself with some time on my hands. I was interested in working on an application that interfaced directly with some sort of small device. The director of our company, Don Coleman, had done a good bit of work with small devices so I spoke to him about the possibility of building a small application around those devices. He agreed to it and offered to assist from the hardware end. Our director of training, Ken Rimple, was researching the Angular 2 JavaScript framework at the time and offered to build the UI for the application. We eventually decided that we would build a demo to display at our booth at the 2016 South by SouthWest conference. This post explains our approach and some lessons learned.
In today’s world of exploding big and fast data, developers who want both streaming analytics and ad hoc, OLAP-like analysis have often had to develop complex architectures such as Lambda—a path for fast streaming analytics using NoSQL stores such as Cassandra and HBase with a separate batch path involving HDFS and Parquet. While this approach works, it involves too many moving parts, too many technologies for ops, and too many engineering hours. Helena Edelson and Evan Chan highlight a much simpler approach to combine streaming and ad hoc/batch analysis using what they call the NoLambda stack (Apache Spark/Scala, Mesos, Akka, Cassandra, Kafka), plus FiloDB, a new entrant to the distributed-database world that combines streaming and ad hoc analytics.
This webinar will describe a reference architecture using the Typesafe Reactive Platform and other tools such as Cassandra, Kafka, and Spark that can be used to build out the real-time organization.
This talk presents Apache Spark, Spark Streaming, Apache Kafka, Apache Cassandra and Akka as supporting Lambda architecture in the context of a fault tolerant, streaming big data pipeline.
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In this talk, Hive and Cassandra author (and Hive committer and PMC member) Edward Capriolo will discuss common big-data software challenges and how they can be solved using both batch and stream processing. Technology focus will primarily be on Apache Kafka for publish-subscribe messaging, Storm for stream processing, and Apache Cassandra as a NoSQL data store.
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From the abstract: The database world is undergoing a major upheaval. NoSQL databases such as MongoDB and Cassandra are emerging as a compelling choice for many applications. They can simplify the persistence of complex data models and offering significantly better scalability and performance. But these databases have a very different and unfamiliar data model and … Read More
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Philly Emerging Technologies Podcast 2011 – #1 Polyglot Persistence for Java Developers Speaker – Chris Richardson, VMware From the ETE Session Abstract Relational databases have long been considered the one true way to persist enterprise data. But today, NoSQL databases are emerging as a viable alternative for many applications. They can simplify the persistence of … Read More
Hoping to get the rest of this videos up before the end of this weekend! This is much more time intensive than I imagined. Anyway, I will stop complaining. Toby DiPasquale was surprised that his room was packed for his talk on Cassandra. Our other NoSql speaker, Geir Magnusson, also had a full room. This … Read More
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Lots of topics this week. Shownotes are available on delicious.com/developernews/13 as well. Submit your story ideas as tag “devnewsideas” and we’ll look for them before each show. South 0.7 Released Python 2.7 Release The future of CSS: The Flexible Box Model MGG 254: Storage, AirDisk, 10.6.3 Woes, SD Cards, and WebKit Groovy 1.7.2 – Three … Read More