August 2019 Summaries

Data breaches pose significant risks to organizations, impacting their bottom line, customer trust, and brand image. To prevent such breaches, a security-first approach to data management is crucial. This involves multiple layers of protection including administrative security (implementing good policies and best practices), technical security (investing in secure hardware and network protection), physical security (access control and surveillance), and database security (choosing a highly secure database). Taking a proactive, layered approach to securing data can help reduce risks and maintain trust with customers.

Apache Pulsar provides consistent publishing latency over time, while Kafka shows periodic spikes in publishing latency. The 99th percentile publishing latency for Pulsar is essentially a straight line for the entire duration of the test, while Kafka has large spikes. Increasing the number of partitions lowers the publishing latency for Pulsar but increases it for Kafka. For end-to-end latency, Kafka beats Pulsar at the lowest partition count, but as the number of partitions is increased, Pulsar's average end-to-end latency approaches 3 ms while Kafka's increases to 11 ms. Disabling flushing to disk for Pulsar has little effect on its performance. In an apples-to-apples comparison with Kafka configured to flush each message to disk, Pulsar is clearly better in terms of latency. Reference(s): [Apache Pulsar](https://pulsar.apache.org/) [Apache Kafka](https://kafka.apache.org/)

Apache Pulsar provides consistent publishing latency over time, while Kafka shows periodic spikes where publishing latency jumps from single digits to over 100 ms. Increasing the number of partitions lowers the publishing latency for Pulsar but has the opposite effect for Kafka. For end-to-end latency, Kafka beats Pulsar at the lowest partition count, but as the number of partitions increases, Pulsar's average end-to-end latency decreases while Kafka's increases. When both systems are flushing to disk, Pulsar is clearly better in terms of latency performance. ```

DataStax, a platinum sponsor of Data Con LA, presented on three major data trends affecting developers. Firstly, the increasing number of inexperienced developers necessitates making working with data easy and intuitive. Secondly, 75% of IT groups are adopting hybrid or multi-cloud strategies, prompting DataStax to bring Cassandra as a service to market this year. Lastly, there's growing interest in multi-model databases that support various ways of accessing data. DataStax is committed to providing the necessary tools for developers to build transformative applications today and in the future.

The Popeyes chicken sandwich shortage highlights the importance of real-time inventory management in today's fast-paced business environment. Companies must have robust technology to keep up with demand, as customers expect near-instant gratification. Macy's faced a similar issue and improved its customer experience by moving to DataStax, reducing catalog refresh time and increasing sales. Businesses need to be prepared for their unique "chicken sandwich" moment to ensure they can meet customer expectations and maintain loyalty.

In today's fast-paced world, users expect websites to load quickly, with nearly half expecting sites to load in two seconds or less. To meet these expectations, modern applications should be built using a hybrid cloud environment and microservices architecture. Hybrid clouds provide more control over on-premise tech infrastructure while leveraging public cloud resources for AI/ML needs. Microservices enable rapid application building and scaling by working on individual features as standalone units. However, data distribution challenges can arise when data is shared among multiple microservices. Containers make microservices architecture easier to manage, but a container orchestration platform like Kubernetes may not be sufficient for simplifying data management in applications built on traditional databases. An Active Everywhere database, such as DataStax Enterprise, built on Apache Cassandra's masterless architecture can help keep pace with performance expectations and ensure smooth data flow in hybrid cloud environments. By using containers, a container orchestration platform, and an Active Everywhere database like DataStax Enterprise, companies can build high-performance applications that drive competitive advantage gains while impressing customers and engaging teams.

Banks have access to an immense amount of customer data and transactional information due to their involvement in digital transformation. However, many financial institutions struggle with effective data management, which is crucial for maintaining customer trust and satisfaction. Legacy systems often hinder banks' ability to handle the growing volume of data and make data-driven decisions quickly. To improve data management, banks should simplify their data architecture, minimize fragmentation, and decommission redundant systems. Additionally, implementing a distributed database management platform can help banks provide customers with modern apps, meet regulatory requirements, and prevent fraud by offering real-time graph capabilities and enterprise-grade security.