The software you build is only as secure as the code that powers it. Learn how malicious code creeps into your software supply chain.
Apache Cassandra combines the benefits of major NoSQL databases to support data management needs not covered by traditional RDBMS vendors.
Developing analytics applications for real-time monitoring and diagnosis of operational activity such as clickstreams or network flows is very different from building for traditional “BI” analytics. First, you need to execute analytics against a live data stream with minimal latency. Second, you need to store petabytes of real-time and historical data together. Third, you need to support hundreds to thousands of users. Last but not least, you need to support a variety of analytics methods, including real-time alerting, dashboards, ad hoc slice and dice, drill downs, search, ideally through a single UI.
In this webinar, one of the creators of Apache Druid, will dive into:
• The current state of real-time and streaming analytics, from stream processing like Spark and Flink to analytics tools including Tableau, Looker, Superset and Imply Pivot.
• The challenges of architecting for real-time analytics, and how combining Kafka and Druid deliver low end-to-end latency at scale.
• Design considerations and details behind Druid's integration with Kafka, Amazon Kinesis, and other messaging technologies for real-time ingestion at scale.
• Architectural considerations for achieving speed and scale for real-time analytics including ad hoc, search and time-based queries.
