Notes about Cutting-Edge Technologies and Everything

This month’s release delivers very useful features like Apache Arrow Flight with Python, full support for CDP 7.1, security enhancements for Oracle connections, a new support bundle and much more. This blog post highlights the following updates: Arrow Flight clients Query support bundle Kerberos support for Dremio-Oracle connections User/job metrics available in the UI Continue reading >>>

According to Gartner: “By 2022, organizations using multiple go-to-market approaches for digital commerce will outperform noncommerce organizations by 30 percentage points in sales growth." Covid-19 has forced many brands to accelerate their digital-first commerce strategies, sooner than they had planned, in an effort to keep up with customer demands and drive revenue.    Whether your brand was an early adopter, or is still struggling to implement a robust digital commerce strategy, the Gartner Predicts 2021:  COVID-19 Drives Accelerated Shift to Digital and Commerce Model Evolution report will help you prepare for 2021. We believe, in this report you will discover:  5 key digital commerce predictions for 2021 and beyond The market implications of these predictions  How you can embrace new market realities to propel your business Get the report >>>

DataOps techniques can provide a more agile and collaborative approach to building and managing data pipelines. The pandemic has accelerated the need for data and analytics leaders to deliver data and analytics insight faster, with higher quality and resiliency in the face of constant change. Organizations need to make better-informed and faster decisions with a focus on automation, real-time risk assessment and mitigation, continuous value delivery and agility. The point of DataOps is to change how people collaborate around data and how it is used in the organization  As a result, data and analytics leaders are increasingly applying DataOps techniques that provide a more agile and collaborative approach to building and managing data pipelines. What is DataOps? Gartner defines DataOps as a collaborative data management practice focused on improving the communication, integration and automation of data flows between data managers and data consumers across an organization. “The poi...

Solutions Review’s Data Integration Tools Vendors to Watch is an annual listing of solution providers we believe are worth monitoring. Companies are commonly included if they demonstrate a product roadmap aligning with our meta-analysis of the marketplace. Other criteria include recent and significant funding, talent acquisition, a disruptive or innovative new technology or product, or inclusion in a major analyst publication. Data integration tools vendors are increasingly being disrupted by cloud connectivity, self-service, and the encroachment of data management functionality. As data volumes grow, we expect to see a continued push by providers in this space to adopt core capabilities of horizontal technology sectors. Organizations are keen on adopting these changes as well, and continue to allocate resources toward the providers that can not only connect data lakes and Hadoop to their analytic frameworks, but cleanse, prepare, and govern data. The next generation of tools will offe...

Our aspiration to augment and improve every aspect of business and life with data, demands a paradigm shift in how we manage data at scale. While the technology advances of the past decade have addressed the scale of volume of data and data processing compute, they have failed to address scale in other dimensions: changes in the data landscape, proliferation of sources of data, diversity of data use cases and users, and speed of response to change. Data mesh addresses these dimensions, founded in four principles: domain-oriented decentralized data ownership and architecture, data as a product, self-serve data infrastructure as a platform, and federated computational governance. Each principle drives a new logical view of the technical architecture and organizational structure. The original writeup, How to Move Beyond a Monolithic Data Lake to a Distributed Data Mesh - which I encourage you to read before joining me back here - empathized with today’s pain points of architectural and or...