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IBM acquires Taos to supplement its cloud expertise as workloads shift

January 15, 2021 Big Data

IBM announced that it’s acquiring Taos, a provider of managed and professional IT services with a strong focus on public cloud computing platforms. At the same time, Deloitte Consulting announced that it has completed its previously announced acquisition of HashedIn Technologies Private Limited, a software engineering and product development firm specializing in cloud native technologies.

Terms of both deals were undisclosed. However, they both come at a time when the number of workloads being shifted to public clouds has accelerated significantly during the COVID-19 pandemic. That shift is altering the center of data gravity in the enterprise in a way that requires IT services providers to add additional application and data management expertise that spans multiple clouds.

Most of the data organizations manage today still reside in on-premises environments. It’s not likely all data will reside on-premises or in the cloud, but rather organizations will find themselves managing data as it ebbs and flows across multiple centers of data gravity, said David Sun, director of corporate business development for IBM Services, in an interview with VentureBeat.

“All our clients are telling us their applications and data will reside in multiple clouds and hybrid cloud computing environments,” said Sun.

Taos, which will operate as an IBM company based in San Jose, California, will remain with IBM after the rest of IBM’s managed services business focused on infrastructure is spun out sometime next year. That relationship is similar to the one IBM is establishing with 7Summits, an IT services provider focused on the Salesforce platform that IBM acquired last week. This follow IBM’s announcement last month that it plans to acquire Nordcloud, a provider of cloud consulting leader in Europe. Overall, mergers and acquisitions among IT services providers is at an all time high.

The challenge enterprise IT teams face today is that there is a general shortage of cloud computing expertise. IBM and other IT service providers are counting on organizations to augment the limited IT expertise and resources they have with external cloud expertise. That expertise is even more sought after now because most enterprise IT organizations don’t have a lot of experience employing cloud native technologies such as containers, Kubernetes, and serverless computing frameworks.

A report published earlier this week by Information Services Group (ISG), a technology research and advisory firm, finds commercial outsourcing contracts with an annual contract value of $ 5 million or more involving as-a-service platform and managed services reached $ 16 billion in the fourth quarter of 2020. That’s up 13% over last year and 9% over the third quarter. Managed services specifically accounted for $ 7.2 billion for the quarter, which according to the report marks the first time managed service deal sizes have returned to their pre-pandemic levels.

A significant percentage of those managed services contracts revolve around cloud computing projects, said ISG president Steve Hall. Managed service providers (MSPs) are trying to strike a balance between a decline in demand for managing on-premises IT infrastructure against cloud computing opportunities that require more software expertise, said Hall. “Many MSPs have been focused on the data center,” he said.

The biggest challenge in the last year has been the simple fact that landing new business typically requires in-person meetings because of the level of trust that needs to be established between service providers and their end customers, noted Hall.

Less clear at the moment is to what degree organizations will ultimately rely more on outsourcing as IT environments become more complex. The overall percentage of IT consumed as a managed service has been relatively small compared to the trillions of dollars in applications and infrastructure that is managed internally by an IT staff. However, Gartner is forecasting that the market for cloud professional services will exceed $ 200 billion by 2024.

Internal IT staff, of course, are often resistant to relying on service providers that they often perceive to be a potential threat to their jobs. The challenge IT services providers face is either overcoming that bias or simply finding a way to bypass that internal IT altogether by establishing a relationship with a business executive or CIO who has come to believe external services providers simply have a level of expertise in one area or another that is needed much sooner than an internal IT team can acquire.

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Teradata Named a Cloud Database Management Leader in the 2020 Gartner Magic Quadrant for Cloud Database Management Systems

January 10, 2021 BI News and Info

Teradata Vantage Also Ranked Highest in Three Out of Four Use Cases in the 2020 Gartner Critical Capabilities for Cloud Database Management Systems for Analytical Use Cases

Teradata (NYSE: TDC), the cloud data analytics platform company, today announced it has been recognized as a Leader in the 2020 Gartner Magic Quadrant for Cloud Database Management Systems, issued November 23, 2020 by analysts Donald Feinberg, Merv Adrian, Rick Greenwald, Adam Ronthal and Henry Cook. Gartner evaluates vendors placed in the Magic Quadrant for Cloud Database Management Systems on completeness of vision and ability to execute following detailed research.

In tandem, Teradata Vantage – the company’s hybrid multi-cloud data analytics software platform – was also recognized with the highest scores in three out of four use cases in the 2020 Gartner Critical Capabilities for Cloud Database Management Systems for Analytical Use Cases, issued November 24, 2020 by analysts Henry Cook, Donald Feinberg, Merv Adrian, Rick Greenwald, and Adam Ronthal. Among the 16 vendors evaluated, Teradata ranked highest in three out of four analytical uses cases – Traditional Data Warehouse, Logical Data Warehouse, and Data Science Exploration/Deep Learning – and ranked second in Operational Intelligence.

“Teradata is committed to providing the best enterprise data analytics in the cloud – period. This means offering our customers a modern data analytics platform that can handle the large and complex workloads that Teradata is known for, and flexible deployment options that don’t limit choice or lock them in,” said Steve McMillan, CEO, Teradata. “We’re the only data warehouse and analytics provider with hybrid multi-cloud offerings across the top three public cloud vendors, providing the same software, features, and experience regardless of environment. This recognition from Gartner validates the strength of our cloud position and further underscores the commitment to our customers to meet them wherever they are on their cloud journey.”

Teradata’s unique hybrid and multi-cloud offerings have become critical differentiators in the Cloud Database Management Systems market. According to Gartner, “The capability to work across intercloud, multicloud and hybrid is increasingly important. This will likely become a prerequisite for these systems.”

Gartner defines the Cloud Database Management Systems (DBMS) as a fully provider-managed public or private cloud software system that manages data in cloud storage. These management systems include specific optimization strategies designed for supporting traditional transactions and/or analytical processing covering one or more of the following seven use cases: Traditional and Augmented Transaction Processing; Traditional and Logical Data Warehouse; Data Science Exploration/Deep Learning; Operational Intelligence; and Stream/Event Processing. Data is stored in a cloud storage tier (e.g., cloud object store, HDFS or other proprietary cloud storage infrastructure), and may use multiple data models — relational, non-relational (document, key-value, wide-column, graph), geospatial, timeseries and others.

Teradata Vantage is the leading hybrid multi-cloud data analytics software platform that enables ecosystem simplification by unifying analytics, data lakes and data warehouses. With Vantage, enterprise-scale companies can eliminate silos and cost-effectively query all their data, all the time, regardless of where the data resides – in the cloud, on multiple clouds, on-premises or any combination thereof – to get a complete view of their business. And now, Teradata is the only data warehouse and analytics provider with hybrid multi-cloud offerings across the top three public cloud vendors, offering maximum flexibility and choice to its enterprise customers.

To learn more visit here.

Gartner Disclaimer
Gartner does not endorse any vendor, product or service depicted in its research publications, and does not advise technology users to select only those vendors with the highest ratings or other designation. Gartner research publications consist of the opinions of Gartner’s research organization and should not be construed as statements of fact. Gartner disclaims all warranties, expressed or implied, with respect to this research, including any warranties of merchantability or fitness for a particular purpose.

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Accelerate Your Enterprise Connectivity with TIBCO Cloud

December 24, 2020 TIBCO Spotfire

Reading Time: 2 minutes

Businesses today face unprecedented challenges responding to market volatility, increasing the importance of business agility as the means to successfully adapt to changing market conditions. One key enabler of business agility is the ability to quickly connect digital assets no matter where they are hosted to create new capabilities or streamline processes.

Accelerated Connectivity = Greater Agility

This growing pressure means many teams are looking for ways to reduce time to market for new connectivity applications as much as possible. As the number of apps created across the company increases, there is an opportunity to streamline development through the reuse of digital assets like APIs. However, the process of tracking down API specs and manually documenting them for consumption in applications can be a time-consuming process that could be spent on more valuable tasks.

New connections could be created even faster if there was a way for developers to easily discover APIs created by different teams across the business to reuse in new integration apps. To help our customers navigate this challenge and reduce time to market, the TIBCO Cloud makes it easy for application developers across your business to share and discover digital assets.

Service Discovery with TIBCO Cloud

The TIBCO Cloud provides a centralized resource for developers to discover and share APIs or other REST services created across the TIBCO Cloud. Using a simple drop-down box, developers can easily find and consume APIs or register a new API that can be reused by other developers.

Accelerate Your Enterprise Connectivity with TIBCO Cloud

In today’s volatile market environment, even the smallest time saving can make a difference as businesses quickly pivot to meet new demands. Utilizing the registry allows developers to focus on more valuable work such as defining and building new product capabilities by promoting the reuse of assets across teams.

The TIBCO Cloud makes it easy for application developers across your business to share and discover digital assets. Click To Tweet

Learn More

For an example using API discovery to streamline integration app development, check out this demo where you will learn how to easily use TIBCO Cloud Integration to automate your marketing lead generation process.

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The TIBCO Blog

Teradata Extends Hybrid Multi-Cloud Capabilities as Customers Move More Workloads to the Cloud

December 21, 2020 BI News and Info

teradata extends hybrid multi cloud capabilities.jpg?width=640&height=336&ext= Teradata Extends Hybrid Multi Cloud Capabilities as Customers Move More Workloads to the Cloud

Teradata QueryGrid Expands Access to Data with Broad Ecosystem Connectivity Enabling Customers to Leverage Data as their Greatest Asset

Teradata (NYSE: TDC), the cloud data analytics platform company, today announced it is extending the hybrid multi-cloud capability of Vantage, enabling customers to access data and analytics across heterogeneous technologies and public cloud providers with new cloud-native capabilities. With the latest release of Teradata QueryGrid, Teradata customers can connect to a vast array of new data sources regardless of where the data resides – in the cloud, on multiple clouds, on-premises, or any combination thereof – for timely and cost-effective analytics.

As organizations increasingly move all or part of their data infrastructure to the cloud or multiple clouds, the number of different data sources and processing engines continues to increase, as does the demand for timely access to data. Companies need to be able to connect to, access and combine information from all data environments at the same time, and at scale, to enable truly effective analytics. This scalable, parallel access to data sources helps companies leverage data as their greatest asset, providing them the information they need to make the best business decisions possible.

“Our customers are the largest and most complex companies in the world, and their data infrastructure and processing environments reflect this complexity. When it comes to these enterprises, it’s not a one-size-fits-all approach to data analytics. They require the ability to query multiple data sources across any environment – in the cloud, multi-cloud, hybrid or on-premises – at the same time, without sacrificing speed or accuracy, and at the lowest possible cost,” said Hillary Ashton, Chief Product Officer at Teradata. “With these cloud-native updates to Vantage’s high-speed data fabric, we continue to give our customers the utmost choice and flexibility in how and when they choose to access their data, while also eliminating data movement and increasing performance – optimizing cost and delivering analytics that matter.”

In addition to providing customers with parallel access to data at scale, Teradata QueryGrid also gives companies the ability to dynamically access data in hybrid and multi-cloud environments and push processing down to where the data resides, thereby reducing data movement, decreasing costs, and reducing time to query. This is especially useful when accessing data in cloud environments where there is an egress fee for exporting out data.

Starburst Enterprise Presto Integration

To extend the company’s reach to the most requested technologies, Teradata has partnered with Starburst Data to integrate a new Presto connector that allows Teradata Vantage users to leverage Starburst Enterprise Presto as part of QueryGrid. This integration with Starburst Enterprise Presto allows Vantage customers to connect to a vast array of new data sources that enable connectivity in a modern cloud architecture, including:

MongoDB,
Amazon Redshift,
Databricks Delta Lake,
Apache Cassandra,
Google BigQuery,
and numerous other data sources and processing engines.

“Starburst is on a mission to enable better and faster decisions by providing a single point of access to all data, no matter where it lives,” said Justin Borgman, Chairman and CEO, Starburst. “By combining Starburst Enterprise Presto with QueryGrid, we are able to offer our joint customers unprecedented reach to high-performance analytics across heterogeneous technologies and deployment platforms.”

Teradata Vantage with QueryGrid is certified on all the major cloud platforms including Google Cloud, AWS and Azure, and now has native integration with key cloud-native technologies such as Google Cloud Dataproc, Azure HDInsight, and Amazon EMR.

For Teradata customers maintaining an on-premises presence or working with hybrid cloud systems, Teradata has also announced an upgrade to its IntelliFlex platform featuring the latest second Gen Intel® Xeon® Scalable processors with 33% more cores than the previous generation. The latest release of the IntelliFlex platform provides customers with up to 30% performance improvement on typical data warehouse workloads and up to 40% on CPU intensive workloads, as well as a 50% increase in memory per node.

Availability

The latest release of Teradata QueryGrid is available globally, today.

The latest update to Teradata Intelliflex is available globally, today.

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Optimize Your Enterprise-scale Processes Easily, and in the Cloud

December 1, 2020 TIBCO Spotfire

Reading Time: 2 minutes

As businesses around the world continue to digitally innovate, are you asking yourself what you’re doing to ensure the continued success of your company as new challenges and opportunities arise?

The Digital Process Automation Opportunity

One of the smartest ways you can improve business operations is with digital process automation, which allows you to manage and optimize your enterprise resources through automated tasks and processes. A good process management platform enables end-to-end execution of long-running processes while providing real-time visibility and traceability.

TIBCO’s flagship deep digital process automation solution has been transformed with both an improved user experience and cloud-native deployment.

Cloud-Native Advancements

Advancing the previously named TIBCO ActiveMatrix BPM, TIBCO BPM Enterprise (BPME) with the new 5.0 version release is now fully cloud-native and can be managed on any CNCF-certified Kubernetes platform. This means scalable, seamless deployments with faster startup and runtime.

Experience Enhancements for Process Automation Experts and Developers

To improve its user experience, the new BPME platform features an out-of-the-box user interface that allows you to manage all applications, as well as perform process management, work management, and case management operations from one easy-to-use UI. Designed and built with TIBCO Responsive Application Mesh in mind, BPME provides users with a blueprint for building modern application architecture for digital process automation. Users can more conveniently build applications using their choice of framework leveraging the RESTful APIs designed with Open API Specification (OAS).

Streamlining Processes Is Easier Than Ever

Providing a cloud-native foundation built on a modern stack, independent of the ActiveMatrix platform, makes it easier to streamline business operations across your enterprise. Existing TIBCO BPM users will also benefit from the latest version, taking advantage of the new capabilities while continuing to run existing workloads on their current platform.

If you’re ready to optimize your business operations, contact us to learn more about TIBCO BPME 5.0 at TIBCO.com. If you’re already a BPME user, reach out to your account representative to discuss migration options.

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The TIBCO Blog

Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

November 29, 2020 Sisense

We live in a world of data: There’s more of it than ever before, in a ceaselessly expanding array of forms and locations. Dealing with Data is your window into the ways data teams are tackling the challenges of this new world to help their companies and their customers thrive.

The data industry has changed drastically over the last 10 years, with perhaps some of the biggest changes happening in the realm of data storage and processing.

The datasphere is expanding at an exponential rate, and companies of all sizes are sitting on immense data stores. And where does all this data live? The cloud.

Modern businesses are born on the cloud: Their systems are built with cloud-native architecture, and their data teams work with cloud data systems instead of on-premises servers.

The proliferation of cloud options has coincided with a lower bar to entry for younger companies, but businesses of all ages have seen the sense of storing their data online instead of on-premises.

The increased interest in cloud storage (and increased volume of data being stored) coincides with an increased demand for data processing engines that can handle more data than ever before.

The shift to the cloud has opened a lot of doors for teams to build bolder products and infuse insights of all kinds into their in-house workflows, user apps, and more.

The cloud is the future, but how did we get here?
Let’s dig into the history of the traditional data warehouse versus cloud data warehouses.

rise of the data team blog cta banner 770x250 1 Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Data warehouse vs. databases

The boosted popularity of data warehouses has caused a misconception that they are wildly different from databases. While the architecture of traditional data warehouses and cloud data warehouses does differ, the ways in which data professionals interact with them (via SQL or SQL-like languages) is roughly the same.

The primary differentiator is the data workload they serve. Let’s explore:

Data warehouse: online analytical processing (OLAP)	Database: online transaction processing (OLTP)
Write once, read many	Write many, read many
Best for large table scans	Best for short table scans
Typically a collection of many data sources	Usually one source that serves an application
Petabyte-level storage	Terabyte-level storage
Columnar-based storage	Row-based storage
Lower concurrency	Higher concurrency
Examples: Redshift, BigQuery, Snowflake	Examples: Postgres, MySQL

Source: https://www.sisense.com/blog/how-to-build-a-performant-data-warehouse-in-redshift/

Given that both data warehouses and databases can be queried with SQL, the skillset required to use a data warehouse versus a database is roughly the same. The decision as to which one to use then comes down to what problem you’re looking to solve.

If there’s a need for data storage and processing of transactional data that serves an application, then an OLTP database is great. However, if the goal is to perform complex analytics on large sets of data from disparate sources, a warehouse is the better solution.

Before we look at modern data warehouses, it’s important to understand where data warehouses started to see why cloud data warehouses solve many analytics challenges.

sisense guide Data Mgmt in the MultiCloud Analytics Era blog banner 770x250 1 Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Traditional vs. Cloud Explained

Traditional data warehouses

Before the rush to move infrastructure to the cloud, data being captured and stored by businesses was already increasing, and thus there was a need for an alternative to OLTP databases that could process large volumes of data more efficiently. The business began to build what are now seen as traditional data warehouses.

A traditional data warehouse is typically a multi-tiered series of servers, data stores, and applications.

While the organization of these layers has been refined over the years, the interoperability of the technologies, the myriad software, and orchestration of the systems make the management of these systems a challenge.

Further, these traditional data warehouses are typically on-premises solutions, which makes updating and managing their technology an additional layer of support overhead.

Cloud data warehouses

The traditional data warehouses solved the problem of processing and synthesizing large data volumes, but they presented new challenges for the analytics process.

Cloud data warehouses took the benefits of the cloud and applied them to data warehouses — bringing massive parallel processing to data teams of all sizes.

Software updates, hardware, and availability are all managed by a third-party cloud provider.

Scaling the warehouse as business analytics needs grow is as simple as clicking a few buttons (and in some cases, it is even automatic).

The warehouse being hosted in the cloud makes it more accessible, and with a rise in cloud SaaS products, integrating a company’s myriad cloud apps (Salesforce, Marketo, etc.) with a cloud data warehouse is simple.

The reduced overhead and cost of ownership with cloud data warehouses often makes them much cheaper than traditional warehouses.

Cloud data warehouses in your data stack

We know what data warehouses do, but with so many applications that have their own databases and reporting, where does the warehouse fit inside your data stack?

To answer this question, it’s important to consider what a cloud data warehouse does best: efficiently store and analyze large volumes of data. The cloud data warehouse does not replace your OLTP database, but instead serves as a repository in which you can load and store data from your databases and cloud SaaS tools.

With all of your data in one place, the warehouse acts as an efficient query engine for cleaning the data, aggregating it, and reporting it — often quickly querying your entire dataset with ease for ad hoc analytics needs.

In recent years, there has been a rise in the use of data lakes, and cloud data warehouses are positioning themselves to be paired well with these. Data lakes are essentially sets of structured and unstructured data living in flat files in some kind of data storage. Cloud data warehouses have the ability to connect directly to lakes, making it easy to pair the two data strategies.

A data-driven future powered by cloud data warehouse technologies

The three most popular cloud data warehouse technologies are Amazon’s Redshift, Snowflake, and Google’s BigQuery. They each handle the same workloads relatively well but differ in how computing and storage are architected within the warehouse.

While they’re all great options, the right choice will be based on the scaling needs and data type requirements of the business. Beyond that, the pricing structure for the three varies slightly, and based on the use case, certain warehouses can be more affordable than others.

As the number of cloud data warehouse options on the market grows, niche players will rise and fall in every industry, with companies choosing this or that cloud option based on its ability to handle their data uniquely well.

Whatever your company does and wherever you’re trying to infuse insights, be it into workflows or customer-facing apps, there’ll be a cloud option that works for you.

The future is in the clouds, and companies that understand this and look for ways to put their data in the right hands at the right time will succeed in amazing ways.

Unlocking Data Storage: The Traditional Data Warehouse vs. Cloud Data Warehouse

Adam Luba is an Analytics Engineer at Sisense who boasts almost five years in the data and analytics space. He’s passionate about empowering data-driven business decisions and loves working with data across its full life cycle.

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How to reverse or restore the order of my cells in Wolfram Mathematica Cloud?

November 27, 2020 BI News and Info

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Intel Geospatial is a cloud platform for AI-powered imagery analytics

October 28, 2020 Big Data

Intel today quietly launched Intel Geospatial, a cloud platform that features data engineering solutions, 3D visualizations, and basic analytics tools for geovisual workloads. Intel says it’s designed to provide access to 2D and 3D geospatial data and apps through an ecosystem of partners, addressing use cases like vegetation management, fire risk assessment and inspection, and more.

The geospatial analytics market is large and growing, with a recent Markets and Markets report estimating it will be worth $ 96.34 billion by 2025. Geospatial imagery can help companies manage assets, for example network assets prone to damage during powerful storms. Moreover, satellite imagery and the AI algorithms trained to analyze it have applications in weather prediction, defense, transportation, insurance, and even health care, namely because of their ability to capture and model environments over extended periods of time.

Using Intel Geospatial, which is powered by Intel datacenters, customers can ingest and manage geovisual data from a mobile- and desktop-accessible web portal. They’re able to view slope, elevation, and other data layers in a 3D environment with zoom, pan, and tilt controls and auto-updated time and date stamps. Moreover, they can analyze the state of various target assets as well as run analytics to extract insights that can then be passed to existing enterprise systems.

Intel Geospatial offers data from satellites, manned aircraft, and unmanned aerial vehicles (UAVs) like drones, with data from Mobileye — Intel’s autonomous vehicle subsidiary — available upon request. The platform’s user interface auto-populates with area-specific datasets and allows for search based on street addresses or GPS coordinates, which are standardized for analytics.

Intel Geospatial offers out-of-the-box algorithms for risk classification, object counting, distance measuring, and public and private record reconciliation. Intel says it’s leveraging startup Enview’s AI to power 3D geospatial classification for faster lidar analytics turnaround. Meanwhile, LiveEO is delivering algorithmic monitoring for railway, electricity, and pipelines.

Intel’s new service joins the list of geospatial products already offered by companies including Google, Microsoft, and Amazon. Google’s BigQuery GIS lets Google Cloud Platform customers analyze and visualize geospatial data in BigQuery. Microsoft offers Azure Maps, a set of geospatial APIs to add spatial analytics and mobility solutions to apps. Amazon provides a registry of open geospatial datasets on Amazon Web Services. And Here Technologies, the company behind a popular location and navigation platform, has a service called XYZ that enables anyone to upload their geospatial data — such as points, lines, polygons, and related metadata — and create apps equipped with real-time maps.

The audio problem:

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Data Analytics Customers Value Choice and Simplicity; Teradata’s New Flexible Cloud Pricing Provides Both

October 21, 2020 BI News and Info

teradata pricing estimate meet business needs Data Analytics Customers Value Choice and Simplicity; Teradata’s New Flexible Cloud Pricing Provides Both

Innovative cloud pricing models include Blended Pricing – for the lowest cost at scale – and Consumption Pricing – for a true pay-as-you-go, usage-based offer

Recognizing that data analytics workloads, usage patterns, and utilization rates can vary widely across an organization, Teradata (NYSE: TDC), the cloud data analytics platform company, today announced flexible cloud pricing options to make it easy for enterprises to grow, and benefit from data analytics in the cloud.

In keeping with Teradata’s aim to provide its customers with simplicity and choice, the company now offers two flexible cloud pricing models: Blended and Consumption. Blended Pricing is best suited for high usage and provides the ultimate in billing predictability while delivering the lowest cost at scale. Consumption Pricing is an affordable, pay-as-you-go option best suited for ad hoc queries and workloads with typical or unknown usage that delivers cost transparency for easy departmental chargeback.

With broad availability of both models, enterprises can expect more choice, lower risk, higher efficiency, and greater transparency from Teradata. These options are crucial in today’s unpredictable market where technologies, supply chains, and customer expectations can shift abruptly, leaving companies with stranded data analytics investments if their software fails to provide enough flexibility to evolve as needs change.

“If 2020 has taught us anything, it’s that change happens fast, and having simple, flexible cloud pricing options gives customers the freedom needed to optimize their data analytics investments,” said Hillary Ashton, Chief Product Officer at Teradata. “Different analytic use cases have vastly different utilization patterns at different points in time, which means that having choice in pricing models enables Teradata to offer the best one for each customer scenario ranging from a small ad hoc discovery system to a large production analytics environment.”

Through decades of catering to data analytics needs spanning dozens of business cycles, Teradata understands deeply what is required for firms to extract the most value from their data. Blended Pricing, which is based primarily on capacity, is optimized for high or predictable utilization. Consumption Pricing is optimized for typical to low utilization, unknown future usage, or for tactical business analytics that include frequent ad hoc queries. The ability for Teradata customers to choose the most appropriate cloud pricing model for data analytic workloads facilitates financial governance and can improve customers’ return on investment.

“The convenience of a true consumption-based pricing model – determined by actual usage of the Vantage platform for running successful queries, rather than just available capacity – is a win for customers who want to better align their investment with specific analytic outcomes,” added Ashton. “It also corrects outdated perceptions about the cost required to become a Teradata customer, since there is now a risk-free, zero down option to pay only for what’s used with Vantage, the industry’s best cloud data analytics platform.”

Analyst firm Gartner predicts in the report, Overcome Economic Uncertainty Through Financial Governance of Your Cloud Data Management Environment, April 2020 that, “By 2022, 75% of organizations using cloud data management will encounter budget overruns resulting in their questioning the value of using cloud services.”

In order to ensure the benefits of deploying in an agile cloud environment are not negated by lack of pricing transparency and cost overruns, Teradata has developed flexible cloud pricing options to meet every company’s use case(s):

Blended Pricing

Teradata’s Blended Pricing option is an enterprise-class pricing model that couples low-cost, predictable reserved compute capacity with on-demand elasticity. Customers pay only for compute resources used or activated, plus storage.

Blended Pricing allows organizations to get cloud elasticity when needed while also benefitting from the lowest cost at scale.

Key benefits of Teradata’s Blended Pricing model include:

Predictability: No billing surprises – unlike other cloud analytics companies that employ inefficient auto-scaling which drives up incremental cost;
Lowest cost at scale: Vantage scales linearly, whereas other cloud analytics companies get increasingly inefficient as they scale; and
Granularity: Vantage offers incremental elasticity, which means not over-provisioning, and thus not over-paying for what’s not needed, unlike alternatives.

Consumption Pricing

Teradata’s Consumption Pricing is an innovative, usage-based option with automatic elasticity in which organizations pay only for compute resources consumed for successful queries, measured down to the kilobyte by user, plus storage. Businesses leveraging Teradata’s Consumption Pricing model need never worry about utilization, system sizing, or resource status since Teradata manages them on their behalf – and they never pay for what they do not use. For a risk-free experience, organizations can even start with zero down and no obligation.

Consumption Pricing makes it easier for customers to experiment with new analytics without fear of leaving money for unused resources on the table. Longtime Teradata customer, True Value Company – one of the world’s leading hardlines wholesalers – is one such customer now taking advantage of the company’s new Consumption Pricing model.

“As we continue on our modernization journey, it’s imperative that we are able to drive new, user-driven analytics capabilities to uncover critical business insights and operational efficiencies, all at an optimized cost,” said Ron Byczynski, Director, Infrastructure, DevOps & QA at True Value. “By leveraging Teradata’s Consumption Pricing model, we will pay only for resources used, allowing us to better streamline our IT investments to ensure maximum ROI.”

Key benefits of Teradata’s Consumption Pricing model include:

Pay only for what is used: Enables improved business efficiency and aligns investment with outcomes while not paying for unused capacity;
Automated elasticity: Since resource sizing is embedded in the offer, now customers no longer need to think about sizing, saving them time on capacity planning;
Cost allocation: Easy departmental chargeback on data analytics costs based on users’ actual resource consumption; and
Zero obligation: A risk-free opportunity to use Teradata Vantage in the cloud.

Vantage: the modern cloud data analytics platform

Teradata Vantage is the modern cloud data analytics platform that unifies data lakes, data warehouses, and analytics with new data sources and types. Built for a hybrid multi-cloud reality, Vantage solves the world’s most complex data challenges at scale.

Both Teradata’s Blended and Consumption Pricing models offer customers the best of what the Vantage platform has to offer, including:

As-a-service delivery: Teradata provisions and manages each customer in a dedicated public cloud environment, enabling users to focus on answers instead of non-differentiating tasks;
Dynamic resource allocation: Field-proven workload management helps customers prioritize users and queries, set guaranteed performance levels, and control spend; and
Software optimization: Sophisticated algorithms minimize compute and IO consumption, streamlining throughput and often eliminating the need to add the non-budgeted compute resources required by others.

Availability

Blended Pricing is now available for every Vantage deployment option worldwide.

Consumption Pricing is available for Vantage on AWS and Vantage on Azure globally. Availability for Vantage on Google Cloud is expected within the first half of 2021.

Resources

Gartner, Overcome Economic Uncertainty Through Financial Governance of Your Cloud Data Management Environment, April 2020.

Note to Investors

This release contains forward-looking statements within the meaning of Section 21E of the Securities and Exchange Act of 1934. Forward-looking statements generally relate to opinions, beliefs and projections of expected future financial and operating performance, business trends, and market conditions, among other things. These forward-looking statements are based upon current expectations and assumptions and involve risks and uncertainties that could cause actual results to differ materially, including the factors discussed in this release and those relating to the timely and successful development, production or acquisition, availability and/or market acceptance of new or existing products, product features and services, and other factors described from time to time in Teradata’s filings with the U.S. Securities and Exchange Commission, including its annual report on Form 10-K for the year ended December 31, 2019 and subsequent quarterly reports on Forms 10-Q, as well as the Company’s annual report to stockholders. Teradata does not undertake any obligation to publicly update or revise any forward-looking statements, whether as a result of new information, future events or otherwise, except as required by law.

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