Notes about Cutting-Edge Technologies and Everything

  Today’s analytics and BI platforms are augmented throughout and enable users to compose low/no-code workflows and applications. Cloud ecosystems and alignment with digital workplace tools are key selection factors. This research helps data and analytics leaders plan for and select these platforms. Analytics and business intelligence (ABI) platforms enable less technical users, including businesspeople, to model, analyze, explore, share and manage data, and collaborate and share findings, enabled by IT and augmented by artificial intelligence (AI). ABI platforms may optionally include the ability to create, modify or enrich a semantic model including business rules. Today’s ABI platforms have an emphasis on visual self-service for end users, augmented by AI to deliver automated insights. Increasingly, the focus of augmentation is shifting from the analyst persona to the consumer or decision maker. To achieve this, automated insights must not only be statistically relevant, but they mu

  As distributed architectures continue to become a new gold standard for data driven organizations, this kind of self-serve motion would be a dream come true for many data leaders. So when the Monte Carlo team got the chance to sit down with Alex, we took a deep dive into how he made it happen.  Here’s how his team architected a hybrid data architecture that prioritizes democratization and access, while ensuring reliability and trust at every turn. Exercise “Controlled Freedom” when dealing with stakeholders Alex has built decentralized access to data at Fox on a foundation he calls “controlled freedom.” In fact, he believes using your data team as the single source of truth within an organization actually creates the biggest silo.  So instead of becoming a guardian and bottleneck, Alex and his data team focus on setting certain parameters around how data is ingested and supplied to stakeholders. Within the framework, internal data consumers at Fox have the freedom to create and use d

  Database management systems continue their move to the cloud — a move that is producing an increasingly complex landscape of vendors and offerings. This Magic Quadrant will help data and analytics leaders make the right choices in a complex and fast-evolving market. Strategic Planning Assumptions By 2025, cloud preference for data management will substantially reduce the vendor landscape while the growth in multicloud will increase the complexity for data governance and integration. By 2022, cloud database management system (DBMS) revenue will account for 50% of the total DBMS market revenue. These DBMSs reflect optimization strategies designed to support transactions and/or analytical processing for one or more of the following use cases:     Traditional and augmented transaction processing     Traditional and logical data warehouse     Data science exploration/deep learning     Stream/event processing     Operational intelligence This market does not include vendors that only provi

  The following post focuses on AWS, MS Azure, and GCP in detail. Learn more about each cloud service and how to choose the best one for your business needs.  Digitalization is being embraced by all of us across the globe, especially cloud computing technology. Whether it's because of its scalability or security or reduced costs, cloud platforms have sprung up to a great extent over a few years. Gone are the days when businesses were confused about whether to choose a cloud service provider or not. Now the confusion surrounds the question of which cloud service provider to use. AWS, Azure, and Google Cloud are our top three contenders. Recently, I happen to stumble upon an informative post focusing on AWS Lambda vs Azure Functions. I must say this one was quite detailed and well-structured. Here they have successfully covered all the aspects that are essential and dominating while we compare lambda vs azure. And I am pretty sure considering both the posts together will act as a s

  Data helps companies take the guesswork out of decision-making. Teams can use data-driven evidence to decide which products to build, which features to add, and which growth initiatives to pursue. And, such insights-driven businesses grow at an annual rate of over 30%. But, there’s a difference between being merely data-aware and insights-driven. Discovering insights requires finding a way to analyze data in near real-time, which is where cloud data warehouses play a vital role. As scalable repositories of data, warehouses allow businesses to find insights by storing and analyzing huge amounts of structured and semi-structured data. And, running a data warehouse is more than a technical initiative. It’s vital to the overall business strategy and can inform an array of future product, marketing, and engineering decisions. But, choosing a cloud data warehouse provider can be challenging. Users have to evaluate costs, performance, the ability to handle real-time workloads, and other par

    Central to treating data as an asset, data monetization should align with familiar research and development (R&D) and product management/marketing approaches. Not to oversimplify the many challenges and activities involved in monetizing data, certain basic concepts will reap significant rewards if executed well.  Evolve from Data Project Management to Data Product Management Although you may already have a data leader such as a chief data officer (CDO), or an analytics leader, the first step toward data monetization is to designate a team tasked with identifying and pursuing opportunities for and generating demonstrable economic benefits from available data assets. They may report to a data and analytics executive, into the enterprise architecture group, a chief digital officer, or perhaps even a business unit head.  Creating a distinct, dedicated data product management role is vital especially when business and data leaders agree on pursuing direct data mo

As an industry, we’ve gotten exceptionally good at building large, complex software systems. We’re now starting to see the rise of massive, complex systems built around data – where the primary business value of the system comes from the analysis of data, rather than the software directly. We’re seeing quick-moving impacts of this trend across the industry, including the emergence of new roles, shifts in customer spending, and the emergence of new startups providing infrastructure and tooling around data. In fact, many of today’s fastest growing infrastructure startups build products to manage data. These systems enable data-driven decision making (analytic systems) and drive data-powered products, including with machine learning (operational systems). They range from the pipes that carry data, to storage solutions that house data, to SQL engines that analyze data, to dashboards that make data easy to understand – from data science and machine learning libraries, to automated data pipe

  Data modeling is the procedure of crafting a visual representation of an entire information system or portions of it in order to convey connections between data points and structures. The objective is to portray the types of data used and stored within the system, the ways the data can be organized and grouped, the relationships among these data types, and their attributes and formats. Data modeling uses abstraction to better understand and represent the nature of the flow of data within an enterprise-level information system.  The types of data models include: Conceptual data models. Logical data models. Physical data models. Database and information system design begins with the creation of these data models.  What is a Data Modeling Tool? A data modeling tool enables quick and efficient database design while minimizing human error. A data modeling software helps craft a high-performance database, generate reports that can be useful for stakeholders and create data definition (a.k.

What Is Mainframe Modernization? Mainframe Modernization entails the process of migrating or improving the IT operations to reduce IT spending efficiently. In the realm of improving, we can define Mainframe Modernization as the process of enhancing legacy infrastructure by incorporating modern interfaces, code modernization, and performance modernization. In terms of migration, it is the process of shifting the enterprise’s code and functionality to a newer platform technology like cloud systems. The strategy employed to modernize Mainframe structures relies on factors like business/customer objectives, IT budgets, and costs of running new technology vs. costs incurred from not modernizing. B enefits of Mainframe Modernization to Cloud Cloud can offer economies of scale and new functions that are not available through mainframe computing. The benefits of cloud technologies and the law of diminishing returns in the Mainframe are calling for an increased demand for migration strate

Databricks SQL   already provides a first-class user experience for BI and SQL directly on the data lake, and today, we are excited to announce another step in making data and AI simple with Databricks Serverless SQL. This new capability for Databricks SQL provides instant compute to users for their BI and SQL workloads, with minimal management required and capacity optimizations that can lower overall cost by an average of 40%. This makes it even easier for organizations to expand adoption of the lakehouse for business analysts who are looking to access the rich, real-time datasets of the lakehouse with a simple and performant solution. Under the hood of this capability is an active server fleet, fully managed by Databricks, that can transfer compute capacity to user queries, typically in about 15 seconds. The best part? You only pay for Serverless SQL when users start running reports or queries. Organizations with business analysts who want to analyze data in the data lake with their