September 2018 Summaries

The relational database management system (RDBMS), which has been the dominant model for database management for almost half a century, is struggling to meet today's business needs. Traditional databases were designed for structured data sets and not for fast-moving, highly distributed and flexible data needs of modern businesses. NoSQL databases have emerged as an alternative solution due to their ability to process massive amounts of distributed data in real time without any downtime. However, these systems still rely on a master-slave data architecture that introduces bottlenecks and potential points of failure. To address this issue, DataStax Enterprise (DSE) was created, an Active Everywhere database built on Apache Cassandra's masterless architecture. DSE provides effortless scaling, continuous availability, and seamless multi-cloud deployments, making it a suitable solution for modern businesses dealing with large amounts of data.

The text discusses a presentation at Distributed Data Summit on five lessons learned from working with distributed databases, specifically Apache Cassandra. It highlights the importance of SQL in NoSQL databases and how it has become an industry standard. The author emphasizes that developing a good database management system requires significant time and resources. They also discuss the need to rethink certain aspects of Cassandra such as tombstones and join support. Furthermore, they talk about the growing trend of hybrid cloud deployments and the potential for optimizations in a cloud-first world. The author concludes by expressing excitement for the future of distributed databases and their role in the cloud age.

A natural language processing project using DataStax Enterprise Analytics with Apache Cassandra, Apache Spark, and several Python tools like Jupyter Notebooks and Pattern is illustrated through the task of determining which movie to watch by analyzing Twitter sentiment. The setup leverages the distributed nature of Cassandra for data storage and the efficient processing capabilities of Spark, integrated seamlessly in DataStax Enterprise Analytics, to handle large datasets. Sentiment analysis is performed on Twitter data to gauge public opinion, using tools like the Twitter API and various Python libraries. The project provides a practical demonstration of data analytics, showcasing how complex technologies can be harnessed simply for real-world applications, with detailed instructions available on GitHub. The installation process for these tools is straightforward, requiring basic configurations, and the setup is demonstrated on a Mac OS, but instructions are applicable to Linux machines as well. The project encourages exploration and modification of the Jupyter Notebook to discover insights beyond traditional movie rating systems like Rotten Tomatoes, illustrating the accessibility and power of data analytics.

DataStax Enterprise (DSE) serves as the foundation for real-time applications at massive scale, with nine of the top 15 global banks relying on it. DSE enables banks and financial institutions to exceed customer expectations through responsive and meaningful engagement in consumer and enterprise applications. It also provides full data autonomy, allowing organizations to retain control and strategic ownership of their most valuable asset in a hybrid cloud world. Hybrid cloud has become the foundation for innovation and agile application delivery for the financial industry, with combined distributed database platforms leading digital innovation in a highly regulated environment. Understanding what a data layer is and how it fits into a hybrid cloud strategy is crucial for banks to continue providing the applications customers expect from financial providers today.

The 'Me' Culture is a growing trend where consumers demand faster and personalized service from companies. This shift in expectations has led businesses to invest heavily in improving their customer experiences. However, many are still falling short due to lack of personalization in their interactions with customers. DataStax conducted a survey to understand consumer preferences around real-time interactions and personalization. The results show that people's willingness to pay more for faster service or higher personalization varies depending on the type of service or product, as well as nationality and age group. Understanding these nuances is crucial for businesses aiming to improve their customer experience initiatives.

Over half of consumers prefer shopping online rather than in-store, with Millennials leading at 67%. This shift has made seamless customer experience a crucial factor in brand loyalty. To support ecommerce in the Right-Now Economy, businesses must provide highly available applications that can access multiple systems of record in real time. A distributed cloud database offers contextual, always-on, real-time, and scalable capabilities to deliver robust experiences to customers. Companies like eBay, Walmart, Macy's, Microsoft, and Comcast have gained a competitive advantage by using distributed cloud databases for their ecommerce innovation, customer experience enhancement, and data insights extraction.

The text discusses the importance of distributed cloud databases in meeting customer expectations, particularly in terms of speed, reliability, and personalization. Distributed cloud databases are characterized by their ability to spread operational data across various physical locations, such as data centers or hybrid clouds. These databases are crucial for applications' success due to five key features they provide: contextual logic for customized experiences, 100% uptime, real-time transaction processing, global availability with localized data, and automatic scaling during growth or high volume traffic. Additionally, data autonomy is a desirable feature that ensures data portability across public clouds and agility in switching providers when needed. DataStax Enterprise is highlighted as an example of such a database solution.

In today's digital era, data has become more valuable than oil and gold, making it crucial for organizations to ensure its security and control. However, many enterprises find their data locked into proprietary cloud databases of tech giants like Amazon Web Services, Google Cloud, and Microsoft Azure. Data autonomy is the concept that allows businesses to retain complete control over their data regardless of where it's stored. This enables them to use any environment for storing, running, and processing data. Data autonomy is essential for comprehensive data security, as it ensures organizations have full control over their most important assets. DataStax Enterprise (DSE) offers a solution by providing an always-on cloud platform powered by the best distribution of Apache Cassandra™, offering scalability, availability, speed, and security required by leading enterprises. DSE allows businesses to choose where they want to store data, enabling them to keep sensitive information secure in their own data centers while moving other data to public clouds.

Advances in computational power, reduced costs, and big data have led to an increased role for artificial intelligence (AI) in modern organizations. In 2017, only 15% of companies used AI, but this is expected to double by the end of 2021. The integration of AI into data management will bring four main changes: faster problem-solving due to automation; real-time streaming analytics for up-to-the-second information; AI-based DevOps workflows for continuous software updates; and drastic industry changes as AI evolves, such as improved healthcare through remote patient monitoring. Overall, AI will lead to new ideas, products, and applications when combined with accurate data and the right processing tools.