Notes about Cutting-Edge Technologies and Everything

 O ver the past few years, companies have been massively shifting their data and applications to the cloud that ended up raising a community of data users. They are encouraged to capture, gather, analyze, and save data for business insights and decision-making. More organizations are leading towards the use of multi-cloud, and the threat of losing data and securing has become challenging. Therefore, managing security policies, rules, metadata details, content traits is becoming critical for the multi-cloud. In this regard, the enterprises are in search of expertise and cloud tool vendors that are capable of providing the fundamental cloud security data governance competencies with excellence. Start with building policies and write them into code, or scripts that can be executed. This requires compliance and cloud security experts working together to build a framework for your complex business. You cannot start from scratch as it will be error-prone and will take too long. Try to in...

Millions of Uber trips take place each day across nearly 80 countries, generating information on traffic, preferred routes, estimated times of arrival/delivery, drop-off locations, and more that enables us to facilitate better experiences for users. To make our data exploration and analysis more streamlined and efficient, we built Uber’s data science workbench (DSW), an all-in-one toolbox for interactive analytics and machine learning that leverages aggregate data. DSW centralizes everything a data scientist needs to perform data exploration, data preparation, ad-hoc analyses, model exploration, workflow scheduling, dashboarding, and collaboration in a single-pane, web-based graphical user interface (GUI). Leveraged by data science, engineering, and operations teams across the company, DSW has quickly scaled to become Uber’s go-to data analytics solution. Current DSW use cases include pricing, safety, fraud detection, and navigation, among other foundational elements of the trip experi...

  Data integration tools address a wide range of use cases that rely on key data delivery capabilities. This research helps data and analytics leaders identify vendors’ relative strengths across these capabilities and select the right tool in support of their data management solutions. Key Findings All data integration tool vendors were rated as “meeting/exceeding expectations” for their support for bulk/batch data movement and streaming data integration. However, support for other data delivery styles (data virtualization and data replication, for example) is less consistently delivered across the range of products evaluated. Active metadata is now critical as organizations continue to focus on metadata-driven optimization and automation of integration flows. The cohort of products in this evaluation averaged 3.3 out of a possible 5.0. While adequate, these capabilities must improve. Data virtualization has become less prominent as a data integration delivery style, with 30% of su...

Protegrity, the data-security solutions provider, today announced support for Amazon Redshift, a fully-managed petabyte scale cloud data warehouse. Organizations with high data-security and IT requirements can now deploy Protegrity’s data de-identification technology in the Amazon Redshift environment. With its format-preserving vaultless tokenization capabilities, Protegrity goes beyond encryption to ensure data is protected at every step of the data lifecycle—from storing and moving to analyzing—no matter where it lives. By allowing protected data to be fully utilized without risk, Protegrity for Amazon Redshift enables customers to drive significantly more value and insights from sensitive data in the cloud. Building on Amazon Redshift’s comprehensive, built-in security capabilities available to customers at no extra cost, Protegrity protects the privacy of individuals by anonymizing data before it reaches Amazon Redshift. The combination of Protegrity and Amazon Redshift allows bus...

Why Read This Report In our 30-criterion evaluation of value stream management (VSM) providers, we identified the 11 most significant ones — Atlassian, Blueprint, CloudBees, ConnectALL, Digital.ai, GitLab, IBM, Plutora, ServiceNow, Targetprocess, and Tasktop — and researched, analyzed, and scored them. This report shows how each provider measures up and helps application development and delivery (AD&D) professionals select the right one for their needs. Strong interest in VSM is driven primarily by three roles: 1) product owners and/or program managers who need data to help drive strategies, set priorities, and unlock team potential; 2) development leaders who use VSM to create connected, automated, and self-governed CI/CD pipelines with observability for improving and accelerating the pace of delivery; and 3) release engineers who use VSM for governance, compliance, and upstream observability to manage risk. (see endnote 3) To serve these roles effectively, customers should look f...

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...