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 invest

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 survey

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