Kategorie: Google Cloud Platform

How Revionics brings advanced analytics to retailers with help from Google Cloud

Editor’s note: Today we’re hearing from Revionics, a lifecycle pricing platform that gives retailers the confidence to ask the “what ifs” and “why nots” that result in profitable pricing strategies. In this blog, the Revionics team shares their cloud data warehouse migration journey and the lessons learned along the way. At Revionics, we’ve been in the data analytics business for nearly two decades, serving retail customers who wanted to leverage their data to drive their pricing strategies. At the company’s founding in 2002, our advanced AI models were probably the most complex in commercial use. Out of necessity, Revionics built the advanced technology infrastructure to handle the data volume and processing for our AI models. Fast forward to today, and cloud infrastructure has finally caught up. The state of current cloud computing infrastructure has opened up new avenues of data exploration for our teams and customers.Researching cloud optionsFrom the beginning, we didn’t just want to solve known problems. We wanted to improve our overall infrastructure to become more nimble to support our customers’ needs, today and in the future. Just like our AI models help retailers focus on what is possible, we need infrastructure that enables our data scientists to create that vision.We had been using on-premises Teradata appliances, and the parallelism and performance had worked well for us. But appliances have hardware and software restrictions that made it hard for us to share data and seamlessly scale. They’re not elastic, and couldn’t offer us the space we needed. We had maxed out the physical storage capacity of the appliances themselves, and we were rapidly approaching the time where we would need to renew the hardware to upgrade and expand performance and capacity. The space required for processing resulted in access constraints to the latest analytical results, impacting decision making based on current information. Replacing appliances with newer ones couldn’t alone solve our elasticity and data access problems, so we started exploring cloud options. In addition, many of our tech-savvy customers have in-house data analytics experts, and we wanted to offer them plug-and-play capabilities on our data warehouse infrastructure. That way, they could dig into their data and respond to market shifts accordingly. Once we learned more about current cloud options, we could visualize many ways to drive our business forward. For example, the elasticity and amount of storage available could let us really accelerate our product development and overall customer success.Cloud migration decisionsPartnering with Google Cloud, we adopted BigQuery. For each client, we built a data lake and a complete structured data warehouse, so every client’s data is isolated and securely accessible to meet client queries. The scale that BigQuery brought became essential when the COVID-19 pandemic wrought huge, overnight changes for our retail clients, who needed to meet unexpected demand while keeping their employees and customers safe. We were able to rapidly update our AI pricing models with our customers’ most current data and give them clarity to make the right pricing decisions. We migrated Teradata SQL to BigQuery SQL, which facilitated a fast migration. Converting Teradata DDL to BigQuery DDL was straightforward—we encountered a few challenges in our view queries due to differences in SQL, but this also gave us an opportunity to learn about how BigQuery works. We’re a lean company, so we needed to move data efficiently without a lot of manual work required. Our DevOps team helped us build tools so we could create script templates for different projects and customer datasets. For us, it was faster to redeploy our own tools than learn new tools. We had a lot of practical conversations discussing our options, and ultimately we did what was least disruptive to our customers and provided the greatest continuity for operational management, while leveraging select services available in Google Cloud.Revionics puts customers first in all that we do. Ensuring they were not impacted during the migration was a top priority, and we worked with our customers closely throughout the process. We migrated following retail’s busiest season, and we carefully performed our due diligence to internally sync and enable our customer go-live in January 2020. Google Cloud helped support our no-downtime migration and ensure business continuity.Google Cloud helped support our no-downtime migration and ensure business continuity.Migration learnings and lessonsMigrating our reports to run on BigQuery required some planning. We primarily did a lift-and-shift migration to minimize the total changes involved; all we had to do was point our existing metadata model to BigQuery. We made adjustments to some data types where necessary, but most of the reports did not need to be updated. However, some queries generate differently in BigQuery, so we took the opportunity to improve the design and performance of selected reports. As an example, our reports sit on views in a separate dataset, and we were able to simply move logic upstream, leveraging a few of the unique capabilities of BigQuery to improve runtime performance.Click to enlargeFor a successful migration, it helps to establish standards and adopt production tools early. We migrated chunks of the data model at a time, allowing us to test and improve through the project. We also took immediate advantage of several BigQuery database capabilities, such as implementing partitioning and clustering. For us, it was the right move and enabled a fast transition. Now, our next step is how to improve our data model. There are changes we can make under the covers that are impactful, such as views and the interface between BigQuery and our reporting platform. Composer and Airflow both came in handy for our data load process. We built extract pipelines to move data from our SQL Servers to Cloud Storage to load into BigQuery, all executed through Composer. We also take full advantage of built-in monitoring and logging tools (formerly known as Stackdriver). On the other side of migrationToday, the infrastructure we’ve created with Google Cloud has helped address the immediate needs we had, and provides the foundation for Revionics to solve new and interesting problems. We’re opening new doors, and Google Cloud has helped improve how we operate our infrastructure, forecast growth, and manage costs. Data access: For example, moving data to construct new analytics had at times been a slow, unwieldy process that could require days of copying and processing. Now, all of a client’s data is securely co-located in BigQuery, enabling immediate access to data for customer-specific analysis without impacting production operations in any way. Data processing happens in seconds and minutes rather than hours and days, whereas to analyze any one customer’s data before our cloud migration, end users often encountered roadblocks to move data off the SQL Server instance. Now, they don’t have to move data at all.Security: We’d been used to focusing on security, at times sacrificing usability out of necessity. With Google Cloud, we use Google’s built-in encryption at rest and in transit without any impact to usability, and with zero configuration requirements or management needed. We’ve improved our security footprint, lowered our management overhead, and improved performance significantly. Additionally, BigQuery makes it simple to triage issues, and we’ve gained significant efficiency in the way we find and solve any issues. The time spent triaging customer questions has been reduced dramatically. Seeing the business impactWhen we first started almost twenty years ago, retailers would set prices for all their products once a year. As retailers adopted our AI-based pricing models, Revionics introduced the ability to automatically model prices every week, and optimize on demand. We have now set the foundation to enable even more advanced modeling and optimization techniques, and are able to model at a deeper and more granular level than ever before, for orders of magnitude greater data volumes, all while improving our processing times. With this new functionality, we will enable retailers to update prices at the speed of their business, providing the ability to test “what ifs” and run pricing scenarios in minutes. Our data scientists can access so much more data than they could before, at speed, and perform data modeling at the transactional level. We’re able to create models now that we’ve been wanting to create for years that are broad and go deeper into the details. This capability is a pillar for Revionics and has helped speed up our product development and unleashed our data scientists. For our customers, this means that we can continually stay ahead of the complexity of modern retail environments, and this new scalability means we can respond to them immediately and help them adapt quickly.   Earlier tools didn’t allow many of our teams to self-serve their analytics needs, but with access to BigQuery, they’re able to do analytics work on their own. From a support angle, that’s been beneficial. Custom reporting requests that used to take hours now are available immediately and securely for the end user through BigQuery.   Combined, this has opened up exciting new roadmap possibilities. We’re looking at improving how we give our customers access to their data, exploring new and intriguing visualizations, all while leveraging the built-in global access and security, giving us a lot of capabilities and potential products. When it comes to the cloud, there’s a lot to learn. If you’re just getting started, we recommend that you master what you can and don’t try to learn everything all at once. To help, allow your teams to explore, then identify the most critical functional and non-functional requirements and stay focused on those to prevent scope creep and help drive success in your own cloud adoption journey.Learn more about Revionics. Thanks for additional contributions from Clinton Pilgrim and Kausik Kannan.
Quelle: Google Cloud Platform

Better BigQuery pricing flexibility with 100 slots

BigQuery is used by organizations of all sizes, and to meet the diverse needs of our users, BigQuery offers highly flexible pricing options. For enterprise customers, BigQuery’s flat-rate billing model is predictable and gives businesses direct control over cost and performance. We’re now making the flat-rate billing model even more accessible by lowering the minimum size to 100 slots, so you can get started faster and quicker.Starting now, you no longer need to purchase a minimum of 500 slots to take advantage of the slots billing model:Purchase as few as 100 slots at a timePurchase in increments of 100 slotsThis change applies to all commitment types—flex, monthly, and annual.Your price per slot remains the same, no matter how many slots you buy. These new billing changes can significantly lower the entry point for slots-based pricing. Here is an example of how this works:100 slots on an annual contract cost $1,700 per month.100 slots on a monthly commitment type cost $2,000 per month.100 flex slots cost just $4 per hour, or less than $0.07 per minute.We’ve heard that a lower-priced offering is especially relevant for organizations in emerging markets. “We have been using BigQuery as our single data warehouse, and using the 1,000 slots flat-rate option effectively,” said Yuan Gao, chief data officer in The Learnings Co., Ltd. “The 100-slot flat-rate offer will give us finer granularity to control the cost. When we have relatively small workloads, we can run them in one project with a 100-slot reservation, without affecting other workloads, with small and predictable costs.” About BigQuery slotsA slot is a virtual CPU used by BigQuery for data processing. When you purchase slots, BigQuery provisions dedicated capacity for your queries to use, and you pay for the seconds your slots were provisioned. For example, when you buy 1,000 slots with an annual commitment, you pay for 1,000 slots for 365 days, and all query costs are included in the price. The more slots you have, the better your throughput and performance. BigQuery’s architecture is serverless and VM-less. Because we don’t keep state in our compute nodes, BigQuery users get unique benefits, like no need to warm up to achieve maximum performance, and no performance cliffs associated with local disk limits.If you are considering switching from the bytes processed billing model to the slots billing model, consider that the bytes processed model gives users up to 2,000 slots or more. therefore, your query performance may not be as good with 100 slots. We recommend you monitor your slot usage and performance for the optimal number of slots to purchase.You can take advantage of the new 100 slots option by enabling BigQuery Reservations, an enterprise-grade workload and capacity management platform. With BigQuery Reservations, you can provision capacity in seconds and departmentalize it into isolated workloads and teams. To get started, head over to Introduction to BigQuery Reservations.To learn more, check out our Google Cloud Next ‘20: OnAir digital conference. This week is focused on data and analytics. My session, DA300: Awesome New Features to Help You Manage BigQuery, discusses in depth how to best utilize BigQuery slots and BigQuery Reservations.
Quelle: Google Cloud Platform

MLB's fan data team hits it out of the park with data warehouse modernization

Editor’s note: In this blog post, VP of Data Engineering at MLB Robert Goretsky provides a deeper dive on MLB’s data warehouse modernization journey. Check out the full Next OnAir session: MLB’s Data Warehouse Modernization.At Major League Baseball (MLB), the data generated by our fans’ digital and in-stadium transactions and interactions allows us to quickly iterate on product features, personalize content and offers, and ensure that fans are connected with our sport. The fan data engineering team at MLB is responsible for managing 350+ data pipelines to ingest data from third-party and internal sources and centralize it in an enterprise data warehouse (EDW). Our EDW is central to driving data-related initiatives across the internal product, marketing, finance, ticketing, shop, analytics, and data science departments, and from all 30 MLB Clubs. Examples of these initiatives include:Personalizing the news articles shown to fans on MLB.com based on their favorite teams.Communicating pertinent information to fans prior to games they’ll be attending.  Generating revenue projections and churn rate analyses for our MLB.tv subscribers. Building ML models to predict future fan purchase behavior.Sharing fan transaction and engagement data from central MLB to the 30 MLB Clubs to allow the Clubs to make informed local decisions.  After a technical evaluation in 2018, we decided to migrate our EDW from Teradata to Google Cloud’s BigQuery. We successfully completed a proof of concept in early 2019, and ran a project to fully migrate from Teradata to BigQuery from May 2019 through November 2019. (Yes, we completed the migration in seven months!) With the migration complete, MLB has realized numerous benefits in migrating to a modern, cloud-first data warehouse platform. Here’s how we did it.How MLB migrated to BigQueryWe ran several workstreams in parallel to migrate from Teradata to BigQuery:Replication: For each of the ~1,000 regularly updated tables in Teradata, we deployed a data replication job using Apache Airflow to copy data from Teradata to BigQuery. Each job was configured to trigger replication only after the data was populated from the corresponding upstream source into Teradata. This meant that data was always as fresh in BigQuery as it was in Teradata. Having fresh data in BigQuery allowed all downstream consumers of the data (including members of the business intelligence, analytics, data science, and marketing teams) to start building all new processes/analyses/reports on BigQuery early on in the project, before most ETL conversion was completed. ETL conversion: We had over 350 ETL jobs running in Airflow and Informatica, each populating data to Teradata or extracting data from Teradata, each of which needed to be converted to interact with BigQuery. To determine the order in which to convert and migrate these jobs, we built a dependency map to determine which tables and ETL jobs were upstream and downstream from others. Jobs that were less entangled, with fewer downstream dependencies, could be migrated first. A SQL Transpiler tool from CompilerWorks was helpful, as it dealt with the rote translation of SQL from one dialect to another. Data engineers needed to individually examine output from this tool, validate results, and, if necessary, adjust query logic accordingly. To assist with validation, we built a table comparison tool that ran on BigQuery and compared output data from ETL jobs. Report conversion: We use two tools to produce reporting for end users—Business Objects and Looker. For each of the Business Objects reports and Looker Dashboards, our business intelligence team converted SQL logic and reviewed report output to ensure accuracy. This workstream was able to run independently of the ETL translation workstream, since the BI team could rely on the data replicated directly from Teradata into BigQuery.End-user training: Users within the marketing, data science, and analytics teams were onboarded to BigQuery early in the project, leveraging the data being replicated from Teradata. This allowed ample time for teams to learn BigQuery syntax and connect their tools to BigQuery.  Security configuration: Leveraging MLB’s existing SSO setup via Okta and G Suite, users were provisioned with access to BigQuery using the same credentials they used for access to their desktop/email. There was no need to set up separate sets of credentials, and users who left the organization were immediately disconnected from data access. The benefits that migration brought MLB Pricing: With BigQuery’s on-demand pricing model, we were able to run side-by-side performance tests with minimal cost and no commitment. These tests involved taking copies of some of our largest and most diverse datasets and running real-world SQL queries to compare execution time. As MLB underwent the migration effort, BigQuery cost increased linearly with the number of workloads migrated. By switching from on-demand to flat-rate pricing using BigQuery Reservations, we are able to fix our costs and avoid surprise overages (there’s always that one user who accidentally runs a ‘SELECT * FROM’ the largest table), and share unused capacity with other departments in our organization, including our data science and analytics teams.Data democratization: Providing direct user access to Teradata was often cumbersome, if not impossible, due to network connectivity restrictions and client software setup issues. By contrast, BigQuery made it trivial to securely share datasets with any G Suite user or group with the click of a button. Users can access BigQuery’s web console to immediately review and run SQL queries on data that is shared with them. They can also use Connected Sheets to analyze large data sets with pivot tables in a familiar interface. In addition, they can import data from files and other databases, and join those private datasets with data shared centrally by the data engineering team.MLB’s central office handles the ingestion and processing of data from a variety of data sources and shares club-specific data with each Club in the initiative known internally as “Wheelhouse.” The previous Wheelhouse data-share infrastructure involved daily data dumps from Teradata to S3, one per Club per dataset, which introduced latency and synchronization issues. The new Wheelhouse infrastructure leverages BigQuery’s authorized views to provide Clubs with real-time access to the specific rows of data relevant to them. For example, MLB receives StubHub sales data for all 30 Clubs, and has set up an authorized view per Club so that each Club can view only the sales for their own team. Due to BigQuery’s serverless infrastructure, there is no concern about one user inadvertently affecting performance for all other users. This simplification in architecture can be seen in the diagrams below:Seeing the IT results from migrating to BigQueryImproved performance: Queries generally complete 50% faster on BigQuery compared with Teradata. In many cases, queries that would simply time out or fail on Teradata (and impact the entire system in the process), or that were not feasible to even consider loading into Teradata, run without issue on BigQuery. This was especially true for our largest data set, which is 150TB+ in size per year and consists of hit-level clickstream data from our websites and apps. This data set previously needed to be stored outside our data warehouse and processed with separate tools from the Hadoop ecosystem, which led to friction for analysts who often wanted to join this data with other transactional data sets.Richer insights via integrations: The BigQuery Data Transfer Service makes it easy to set up integrations with several services MLB currently uses, including Google Ads, Google Campaign Manager, and Firebase. Previously, setting up these kinds of integrations involved hand-coded, time-consuming ETL processes. Looker, our BI tool, seamlessly integrates with BigQuery and provides a clean and highly performing interface for business users to access and drill into data. Third-party vendor support for BigQuery is strong as well. As an example, our marketing analytics team is able to use the data ingested from Google Ads to inform advertising spend and placement decisions. Reduced operational overhead: With Teradata, MLB needed a full-time DBA team on staff to handle 24×7 operational support for database issues, including bad queries, backup issues, space allocation, and user permissioning. With BigQuery, MLB has found no need for this role. Google Cloud’s support covers any major service issues, and former administrative tasks such as restoring a table backup can now be done easily by end users, letting our IT teams focus on more strategic work.Increased developer happiness: Our data engineering, data science, and analytics staff were often frustrated by the lack of documentation and subtle gotchas present within Teradata. Given that Teradata was mainly used within larger enterprise deployments, online documentation on sites such as Stack Overflow was limited. In contrast, BigQuery is well-documented, and given the lack of barriers to entry (any Google Cloud user can try it for free), there already seem to be more resources available online to troubleshoot issues, get answers to questions, and learn about product features.Accelerated time to value: No downtime or upgrade planning is needed to immediately take advantage of new useful features that are added on a regular basis by the BigQuery engineering team.Seeing the business impact from BigQueryWith our migration to BigQuery complete, we’re now able to take a more comprehensive and frictionless approach to leveraging our fan data to serve our fans and the league. A few projects that have already been facilitated by our move to BigQuery are:OneView: This is a new initiative launched by our data platform product team to compile over 30 pertinent data sources into a single table, with one row per fan, to facilitate downstream personalization and segmentation initiatives. Previously, we would have spent a long time developing and troubleshooting an incremental load process to populate this table. Instead, with the power of BigQuery, we’re able to run full rebuilds of this table on a regular basis that complete quickly and do not adversely affect performance of other data workloads. We’re also able to leverage BigQuery’s Array and Struct data types to nest repeated data elements within single columns in this table to allow users to drill into more specific data without needing any lookups or joins. This OneView table is already being used to power news article personalization.Real-time form submission reporting: By using the Google-provided Dataflow template to stream data from Pub/Sub in real time to BigQuery, we are able to create Looker dashboards with real-time reporting on form submissions for initiatives such as our “Opening Day Pick ‘Em” contest. This allows our editorial team to create up-to-the-minute analyses of results.With our modern data warehouse up and running, we’re able to serve data stakeholders better than ever before. We’re excited about the data-driven capabilities we’ve unlocked to continue creating a better online and in-person experience for our fans.
Quelle: Google Cloud Platform

12 smart analytics sessions to check out at Next OnAir

Business leaders are looking to generate unmatched value from their data to drive competitive advantage and accelerate digital transformation. Digital innovators are tapping into new customer opportunities, using data to deliver new experiences and disrupt industries. At Google Cloud, we’re building a smart analytics platform that is open, intelligent, and flexible and offers a simple path to modernization. As part of Google Cloud Next ‘20: OnAir, we’ll spend this week exploring how you can incorporate analytics into your business. Take a look at these can’t-miss sessions (available starting at 9am PT tomorrow, August 11, and available on-demand afterward), or explore the full schedule. This year, we’re also highlighting our recent acquisition of Looker, a leading business intelligence and analytics platform. Looker serves as an example of our ongoing investment to support our customers’ success with data modernization and maximizing data’s potential–no matter where it resides. What’s new and what’s next in smart analytics: Join Debanjan Saha, GM and VP of Engineering for Data Analytics at Google Cloud to hear about the latest innovations in Google Cloud’s smart analytics platform. Many data platform architectures in use today were designed 20 years ago, and they’re struggling to solve modern business problems. You’ll get details on our data analytics solutions, and learn about some of our latest innovations, plus hear from current customers like Procter & Gamble on how they’re using analytics for product development and consumer experience transformation.Smart analytics: deep dive on roadmap: Take a comprehensive tour through the technical value of Google Cloud’s smart analytics platform. You’ll hear from Director of Product Management Sudhir Hasbe on what’s new and what’s next in Google Cloud’s smart analytics portfolio across products like BigQuery, Dataflow, Dataproc, Data Fusion, Pub/Sub, Data Catalog, Dataprep, and Looker. What’s new in BigQuery, Google Cloud’s modern data warehouse: There’s a lot of pressure on businesses to access fresh information to make real-time decisions—and start to go beyond real time to make predictions based on data. In this session, you’ll hear about new features in BigQuery, our cloud data warehouse designed to help you adapt and succeed in quickly changing environments. Analytics in a multi-cloud world with BigQuery Omni: Using multi-cloud is a necessity for many businesses, and can help speed up digital transformation journeys. Join this session to learn about our recently announced BigQuery Omni, a flexible, multi-cloud analytics solution, powered by Anthos, that helps organizations analyze data in AWS and Azure (coming soon).Data QnA: how Veolia democratizes access to BigQuery: In this session, you’ll get details on the newly announced Data QnA, a service that empowers business users to simply ask questions of their data in BigQuery, using natural language, and get an answer immediately. See how Veolia, a global leader in water, waste and energy resource management solutions, is using Data QnA to democratize access to analytics across their teams.Looker’s roadmap: 2020 and beyond: Data and analytics platform Looker, a recent addition to Google Cloud, brings data experiences that fit the way people work. In this session, you’ll get a look at Looker’s product roadmap, including upcoming features and new projects.Building data lakes on Google Cloud: Data lakes let you aggregate data and analyze it efficiently using cloud-native or open source tools, no matter where data is managed. Join this session to hear from one company that built a data lake using Google Cloud technologies and learn more about how you can simplify data lake deployment and management within your organization.Easy access to stream analytics with Google Cloud: Real-time is growing at an exponential rate, and streaming analytics for app and user events keep growing in importance. Customers expect businesses to be 0aware and prepared in the moment. Join this session to learn about cloud developments that can help you create and manage real-time, data-driven experiences. Best practices from experts to maximize BigQuery performance (featuring Twitter): BigQuery lets you deploy complex workloads and run fast analytics on your data. In this session, learn tips and tricks from Google Cloud and Twitter engineers on how you can maximize the query performance of your data warehouse and speed up analytics within your environment. Looker and BigQuery: enterprise digital transformation at Sunrun: See how solar electricity provider Sunrun used Looker and BigQuery to modernize its legacy systems. Sunrun’s cloud migration helped teams reduce the complexity of ETL processes, improve database performance, and adapt quickly to changes in data infrastructure.   Awesome new features to help you manage BigQuery: Our serverless analytics platform BigQuery brings scalability and performance to all kinds of users. In this session, you’ll learn about details related to pricing, administration, and monitoring in BigQuery, all designed to provide flexibility that works best for your business and users.BigQuery ML: what’s new: BigQuery lets you build and operationalize machine learning models in BigQuery using standard SQL. Join this session to hear about new features and model types in BigQuery ML, and hear how Demandbase has used BigQuery ML successfully for predictive analytics.In addition to these, you’ll find 30 additional data analytics sessions from Googlers, customers, and partners exploring what’s new in data-driven initiatives. And check out study jams, talks by developer advocates, and other ways to learn more.
Quelle: Google Cloud Platform

Google Cloud Partner opportunity to more than triple by 2025, according to IDC study

Thanks to my role at Google Cloud, I’m privileged with a front-row seat to all of the amazing work our partners do on behalf of customers. Our partners play a critical role in delivering Google Cloud technology and solutions to organizations all over the world, so I am delighted to share the findings of a new study from IDC that shows Google Cloud is thriving, growing, and driving significant economic benefit for partners. To summarize IDC’s study in a few words: Demand for cloud technology and services is growing rapidly, as businesses embark on digital transformations, and Google Cloud partners are particularly well-positioned to help customers plan and execute their digital transformation strategies.According to IDC, “growth mode” continues for the IT market overall, and importantly, demand for cloud infrastructure, and cloud capabilities in areas like data analytics, artificial intelligence, IoT, and security is growing at an even faster pace. I’ve seen firsthand how the global pandemic has further increased businesses’ needs for these capabilities, as they seek to quickly adapt or even to take this opportunity to accelerate their digital transformations.As IDC reports, “This is good news for Google Cloud partners, who by their nature are engaged across many of these technologies.” In fact, the analyst firm’s study forecasts that partners’ revenue from Google Cloud-related opportunities will more than triple by 2025, representing a tremendous opportunity for the ecosystem overall in building out their Google Cloud practices.IDC’s research also found that Google Cloud partners’ businesses are growing at a fast clip. On average, partners are growing their Google Cloud businesses at a rate of 35% year-over-year, with a significant group of partners—one in five—growing even faster, at 75%-plus year-over-year.All of this translates to a thriving ecosystem around Google Cloud technologies, and it’s encouraging to see how much value is passed through to our partners. Globally, for every $1 of Google Cloud technology sold in 2020, partners will generate $5.32 through their own offerings, services, and IP. IDC expects this number to grow, reaching $7.54 by 2025. IDC’s study also provides insights into several other benefits that partners are seeing from working with Google Cloud:Google Cloud partners are leading the way on digital transformation. IDC places a full half (50%) of Google Cloud partners in the “late stage of digital maturity,” and more than a third of Google Cloud partners have “fully integrated digital into their strategies and businesses,” meaning customers can trust Google Cloud partners to bring very strong expertise with modern cloud technologies and solutions.At Google Cloud, we are focused on delivering end-to-end, best-in-class solutions. Many of our partners are extending these solutions, and IDC’s study found that these partners, who are creating their own unique IP around Google Cloud, are seeing very strong margins associated with these products. Partners are benefitting from our goal of 100% partner attach on all customer sales, and partners are seeing strong margins across multiple activities, including resale, IaaS, PaaS, and SaaS add-ons, IT services, business services, and support for hardware and networking.We have a tremendous opportunity together to help customers across industries and around the world transform their organizations with the cloud. I’m proud of the amazing work that our partners are doing, and we’re committed to continuing to work closely with partners to expand the Google Cloud economy together. To read IDC’s findings in full, you can download the report here.
Quelle: Google Cloud Platform