Unlock Powerful Insights: Monitor Cloud Expenses with BigQuery’s Data Export
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TL;DR – Regular file export for Google Cloud billing is going away on May 15th! Try out this interactive tutorial to set up exporting and explore your billing data with BigQuery
You may have seen an email from Google Cloud reminding you to set up billing data export to BigQuery, and letting you know that file export options are going away. More specifically:
Starting May 15, 2023, your daily usage and cost estimates won’t be automatically exported to a Cloud Storage bucket. Your existing Cloud Storage buckets will continue to exist, but no new files will be added on an ongoing basis.
In fact, I even received one of these notifications since I had an old billing account (or two) that still had file export enabled. Even though the file export is going away, exporting your billing data to BigQuery gives you more detail and more capabilities to analyze, so it’s a big upgrade.
Start from the start
While Google Cloud does offer a free trial and free tier of services, it shouldn’t come as a surprise that most cloud services cost money to use. With the unique properties of the cloud, it’s much easier to bill for exact usage of various services, like pay-per-second for virtual machines. However, this can also become an overwhelming amount of information when a large number of services are being used across multiple teams. In order to make sure you understand your billing data, Google Cloud offers that data through direct reporting in the Cloud console, file exports, and exporting to BigQuery.
Most importantly, the file export for Cloud Billing data is going away on May 15th (the file exports for specific reports, like the cost table, aren’t going away). This was an option for billing accounts to generate daily CSV or JSON files into a Cloud Storage bucket so you could download the files and analyze them as needed. While these exports were definitely helpful for looking at your billing data, over time they couldn’t keep up with the complexity and volume of data that comes out of a billing account.
The most important thing to note here is simple: this export option is being deprecated entirely, so if you’re currently using it (and even if you’re not), you should make sure exporting to BigQuery is enabled.
Large data, large queries
Exporting from your billing account into BigQuery has been an option for the past few years, and now more than ever it’s the central option for managing your billing data. Of course, BigQuery is a natural home for large datasets with the scale and granularity of your billing data. In addition to that, exporting to BigQuery also happens more frequently than a daily file export, so you can do cost anomaly detection much faster.
As teams and organizations become larger, it can be increasingly difficult to figure out which groups are responsible for different costs. If your organization is using a chargeback model (where one group pays for all resources and then breaks down cost details for individual teams/groups to be responsible for their usage), then using BigQuery is the best way for you to accurately collect and analyze costs. On top of being able to query directly against your data, BigQuery also makes it much easier to integrate with other tools, like Looker or Looker Studio for visualization.
The actual data being exported to BigQuery are hourly records of every service your billing account is responsible for, along with detailed information like the SKU, what type of unit is being measured, and how many of that unit you’re paying for.
I suspect most folks using the file export (and probably most folks overall) will be fine with the standard export for BigQuery, and can use the detailed cost and pricing options if they need even more details. You can find some more details on all the exports in this blog post.
With the file export going away, it’s also important to mention: exporting your Cloud Billing data to BigQuery starts when you enable it, and doesn’t include older data. So make sure you set yourself up for future success by enabling billing export as soon as possible. Try using our built-in walkthrough to help you enable the export and to get started analyzing the data! You can also read more in the documentation.
“It’s an Astonishing Difference”: What Data Operation Execs say About Google Cloud’s Data Warehouse
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The popularity of meal kit delivery services has surged in recent years as consumer attitudes toward home cooking and grocery shopping have shifted. As a pioneer in the category, Blue Apron helps its customers create incredible home cooking experiences by sending culinary-driven recipes with high-quality ingredients and step-by-step instructions straight to customers’ doors. Blue Apron also offers a monthly wine subscription service and a la carte culinary tools and products through its marketplace.
If that sounds simple, it isn’t.
Ingredients for the meal kits must be sourced at the right time, quality, and price. Orders must be packed efficiently and in exactly the right proportions. Most importantly, meal kits must be delivered to the customer fresh and on time.
To meet these criteria and make data meaningful and intuitive to its managers, one of the tools Blue Apron relies on is Looker, an analytics platform that lets business users explore data and ask sophisticated questions using familiar terms. Looker integrates its solution with Google Cloud Platform to help customers modernize their analytics.
“The combination of Looker and Google BigQuery is powerful, allowing us to get data-hungry analysts essential information much faster. Because we choose to pay by the query, it’s also flexible and cost effective—plus storage is cheap, so we can just put data in and query what we need.”
—Sam Chase, Tech Lead, Data Operations, Blue Apron
Blue Apron previously used Looker with a single database instance hosted on another cloud provider. As data volumes grew and queries became more complex, it became difficult to scale. Blue Apron’s only options were choosing ever-larger server classes and increasing storage throughput by purchasing a higher number of provisioned IOPS. To improve speed, scalability, and cost efficiency, Blue Apron moved its data warehouse to Google BigQuery.
“The combination of Looker and Google BigQuery is powerful, allowing us to get data-hungry analysts essential information much faster,” says Sam Chase, Tech Lead, Data Operations at Blue Apron. “Because we choose to pay by the query, it’s also flexible and cost effective—plus storage is cheap, so we can just put data in and query what we need.”
“After we moved to Google BigQuery, query time was reduced exponentially. It’s an astonishing difference, allowing us to run 300 queries per day.”
—Sam Chase, Tech Lead, Data Operations, Blue Apron
The analytics platform of the future
When you’re making business decisions about a customer’s dinner, speed matters. Looker takes full advantage of the power of Google BigQuery, making it easy to build a data exploration platform.
Blue Apron’s applications publish event data to Kafka—approximately 140 million events per day—and data is then streamed into Google BigQuery, which performs lightning-fast queries on both streamed and static data. Now, business users and analytics teams can make decisions based on near real-time information in Looker, instead of waiting until the next business day for results.
“After we moved to Google BigQuery, query time was reduced exponentially. It’s an astonishing difference, allowing us to run 300 queries per day,” says Sam.
Previously, Blue Apron spent up to a week out of every month optimizing its data warehouse to attempt to improve query performance. With Google BigQuery, all maintenance is handled by Google, reclaiming 25% of up to two engineers’ time. Even when multiple people are using Looker concurrently, query performance never degrades and storage never runs out.
“Because Google BigQuery is architected as a giant, shared cluster, growth is smooth,” says Lloyd Tabb, Founder and CTO of Looker. “Like a race car going from 0 to 120 mph, there are no shift points, just smooth acceleration. To us, it looks like the future.”
An empowering, integrated toolset
Looker takes advantage of aggressive caching and support for date-based table partitioning in Google BigQuery to increase performance, simplify the load process, and improve data manageability. By partitioning data by time, Blue Apron can also take advantage of better long-term storage pricing without sacrificing query performance. When using Google BigQuery with Looker, analysts can easily see how much data is going to be scanned before each query is run.
Blue Apron is also using Looker for Google BigQuery Data Transfer Service to provide actionable analytics for all of the company’s Google marketing data from Google AdWords and DoubleClick by Google in one place to understand campaign performance across channels, saving its data operations team months of work. Using Looker Blocks, marketers can quickly make sense of the data with reports and dashboards, and set alerts when campaign performance hits certain thresholds.
“Everyone at Blue Apron is excited about using Google BigQuery with Looker. Business users and marketers are more empowered to look for answers, instead of waiting for analytics teams. Because users know they can get results rapidly, our business processes are evolving and improving.”
—Sam Chase, Tech Lead, Data Operations, Blue Apron
Looker Blocks for Google AdWords and DoubleClick by Google provide all the analysis you’d get straight from the Google console, plus additional value-add analysis that’s impossible to replicate without SQL. Complex metrics such as ROI on ad spend, flexible multi-touch attribution, and predictive lifetime value empower marketers with a better understanding of their customers and where to spend their next dollar.
In addition to these turnkey dashboards and pieces of analysis, marketers can customize views to meet their unique needs and workflows. These capabilities help the Blue Apron marketing team make decisions regarding the allocation of spend to maximize customer acquisition and retention.
“Everyone at Blue Apron is excited about using Google BigQuery with Looker,” says Sam. “Business users and marketers are more empowered to look for answers, instead of waiting for analytics teams. Because users know they can get results rapidly, our business processes are evolving and improving.”
For data cleansing and transformation, Blue Apron uses Google Cloud Dataproc to run fully managed Apache Spark clusters on Google Cloud Platform. It’s also leveraging Google BigQuery integration with G Suite to bring data into Google Sheets for further distribution and analysis.
“Transferring data between Google tools is fast because it all happens on the Google network,” says Sam. “We can pull data from Google BigQuery, run transformations with Spark, and then write it back to Google BigQuery. That’s very helpful in providing our business users and data analysts with the richest, most current data.”
A perfect match for better insights
As Blue Apron seeks to expand its reach and deepen its engagement with customers, it is making Google BigQuery and Looker available to more users, providing a high-quality interactive analytics experience. “Our ability to pull a lot of data in and compute fast results affects everyone in our company,” says Sam. “Using Google BigQuery and Looker to iterate quickly and build new models to make our operations more efficient will directly impact our customers.”
For Looker, Google BigQuery represents the next step in data warehouse evolution. “Google BigQuery is a perfect match for Looker, combining easy setup with near infinite scale-out and elasticity,” says Lloyd. “People can make smarter decisions faster that directly benefit their business and customers.”
What’s Google Cloud Firestore Database and What are it’s Benefits for Business and Developers?
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Cloud Firestore is a NoSQL document database that simplifies storing, syncing, and querying data for your mobile and web apps at global scale.
Cloud Firestore is a fast, fully managed, serverless, cloud-native NoSQL document database that simplifies storing, syncing, and querying data for your mobile, web, and IoT apps at global scale.
Its client libraries provide live synchronization and offline support, while its security features and integrations with Firebase and Google Cloud Platform (GCP) accelerate building truly serverless apps.
Here’s other stuff it’s good at:
Sync data across devices, on or offline
With Cloud Firestore, your applications can be updated in near real time when data on the back end changes. This is not only great for building collaborative multi-user mobile applications, but also means you can keep your data in sync with individual users who might want to use your app from multiple devices.
With Firebase Realtime Database, we felt we had built the best force-plate testing software on the market. Thanks to Cloud Firestore, in only two weeks, we built a system that’s significantly better and includes features we never thought possible to ship on Day 1.
Chris Wales, CTO, Hawkin Dynamics
Cloud Firestore has full offline support, so you can access and make changes to your data, and those changes will be synced to the cloud when the client comes back online. Built-in offline support leverages local cache to serve and store data, so your app remains responsive regardless of network latency or internet connectivity.
Simple and effortless
Cloud Firestore’s robust client libraries make it easy for you to update and receive new data while worrying less about establishing network connections or unforeseen race conditions. It can scale effortlessly as your app grows. Cloud Firestore allows you to run sophisticated queries against your data. This gives you more flexibility in the way you structure your data and can often mean that you have to do less filtering on the client, which keeps your network calls and data usage more efficient.
Enterprise-grade, scalable NoSQL
Cloud Firestore is a fast and fully managed NoSQL cloud database. It is built to scale and takes advantage of GCP’s powerful infrastructure, with automatic horizontal scaling in and out, in response to your application’s load. Security access controls for data are built in and enable you to handle data validation via a configuration language.
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Business Modernization with Oracle Workloads in Google Cloud
With today’s ever-changing business dynamics, business continuity is a key focus for all of us, and enterprise databases like Oracle play a critical role. We’ll explore how running Oracle workloads in Google Cloud creates a variety of solutions, from a Bare Metal Solution to open source and cloud-native databases, to help reduce your operational overhead and keep your business running smoothly.
This session is led by Paul Miller, Head of Solution Management for DB, Google Cloud. Paul is an accomplished data management leader with over 20 years experience in application development, data management, and solution engineering. He is an emphatic team builder who loves to figure things out. Paul has deep experience on databases from leading enterprise software companies such as Google Cloud and Oracle.
How One Company Uses AI and Data Analysis to Boost Revenue
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AI, deep learning, and image recognition is transforming the shopping experience. These technologies enable consumers to use product images or screenshots rather than text to search for similar products. This improves the customer experience and enables retailers with online and offline outlets to provide a genuine omnichannel experience.
The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.
—Renjie Yao, Data Platform Lead, ViSenze
Visual commerce provider ViSenze is helping some of the world’s leading retailers improve conversion rates through image-based search.
The business’s products also enable media companies to use the platform to turn images and videos into engagement opportunities—driving new and incremental revenues.
Created through NExT—a research center established by the National University of Singapore and Tsinghua University of China—ViSenze now operates in the United States, United Kingdom, India, China, and Singapore. The business is backed by Japan-based internet and ecommerce company Rakuten and cross-border investment specialist WI Harper Group.
Growth in SMB and Mobiles
Renjie Yao, Data Platform Lead at ViSenze, sees opportunities for growth in the small-to-medium business sector, where companies do not have the resources to build similar technologies, and with mobile device OEMs to integrate ViSenze natively on smartphones.
Phone owners can activate a “shopping lens” on camera and gallery apps to capture an image of a product. They then receive matching results from more than 800 partner merchants and retailers and can then click through to product pages on partner apps or mobile websites. Alternatively, they may use a photo to compare products sold on different sites or shop matching styles.
“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs.”
—Renjie Yao, Data Platform Lead, ViSenze
The ViSenze API analyzes the contents of a selected or clicked image and sends the information back to the organization’s visual commerce platform. The platform feeds back similar results based on that information.
The ViSenze offering also extends to image analysis for the tagging of product attributes—such as a white turtleneck cardigan with full sleeves—to provide an improved search experience.
Data Vital to ViSenze
Capturing and analyzing large volumes of data is integral to ViSenze. “We have to understand how consumers interact with our customers’ ecommerce websites and apps,” says Yao. “For example, we need to know who has looked at a particular pair of jeans on a website and whether that visit led to a conversion. We can then tell that customer whether they need to make more stock available.”
ViSenze also relies on data to provide high-quality training for its image recognition models and its domain-specific models for online retail.
Protect Customer Data
Data is vital to ViSenze—but customer privacy is most important. “All the data we collect is transparent to our customers, meaning they can decide what they do not want us to collect. In addition, all personal data processing complies with privacy protection regulations in each region, such as the General Data Protection Regulation in Europe.”
A Quick Move to the Cloud
ViSenze started operations using servers, storage, networking, and associated systems in an on-premises data center operated by NExT.
However, to support rapid growth, the business decided to move its workloads to the cloud. ViSenze opted for a multi-cloud architecture, using in part a Google Cloud data infrastructure.
“Our research found Google Cloud provided a complete, integrated ecosystem rather than a disparate collection of tools and components, and so was ideal for our needs,” says Yao. “We could connect different components with the click of a mouse.” Further, the business found it could easily configure rules and pipelines to route data logs to relevant Google Cloud services.
The review found Google Cloud’s extensive managed services would also remove administration and maintenance tasks from ViSenze’s in-house technology team—freeing team members to focus on more valuable tasks.
In addition, Google Cloud provided the security features—including custom hardware running hardened operating systems and file systems and encryption of data at rest and in transit—needed to protect sensitive information. Finally, the location of Google Cloud regions in several countries would enable the business to meet regulatory and data sovereignty requirements.
A Three-Month Implementation
ViSenze opted to move to Google Cloud in mid-2017 and completed a three-month implementation using internal resources. “The process was very smooth and intuitive, and we had no problems building our entire data platform within Google Cloud,” says Yao.
The business now uses an architecture comprising Google Kubernetes Engine to manage and orchestrate Docker containers running in Google Cloud Platform; BigQuery to provide an analytics data warehouse, with Google Data Studio providing customizable visualization and reports; Stackdriver to monitor and manage virtual machine instances and services inside Google Cloud; Cloud SQL to manage its relational databases for real-time analytics; Compute Engine to provide compute resources; Cloud Storage to store files and objects; Cloud Pub/Sub to provide real-time messaging between applications; and Cloud Functions to build event-driven applications.
After collecting the request logs of users in virtual machine instances and Docker containers, ViSenze distributes them in three directions. “We export raw logs into Cloud Pub/Sub for indexing inside an Elasticsearch search engine, and to a BigQuery data warehouse for further analytics,” explains Yao. “We also use Cloud Functions-created applications to obtain the logs from Cloud Pub/Sub to perform some real-time calculations.”
“We are currently using Airflow workflow management on Compute Engine as our hosted ETL platform, but are likely to move to Cloud Composer in future.”
500 Million Records Per Day
With Google Cloud providing its data infrastructure, ViSenze is well positioned to meet internal and customer demands for more granular insights. The business is now processing 500 million records per day through BigQuery and saves up to one year’s aggregated data—excluding any personal data—in the data warehouse for analysis.
The nature of ViSenze’s business means most reports are generated for data processed on an hourly, daily, or monthly basis. “BigQuery is extremely stable and performance optimized, regardless of the volume of data it processes,” says Yao. “Across BigQuery and other Google Cloud Platform services, we’ve recorded 99.99% availability over the past year.”
The lack of complexity and the ease of use of BigQuery has enabled ViSenze to reduce its data infrastructure and management costs by 30%–50%, and scale up without incurring downtime.
“With BigQuery, we have saved the equivalent of two full-time engineers and now need only half of one person’s time to maintain our whole data platform,” says Yao.
“In addition, BigQuery integrates closely with Data Studio, enabling non-technical people in our product and business teams to create dynamic, detailed analysis dashboards. We now use Data Studio to create nearly 50 separate reports.”
The business has now grown to offer access to more than 1 billion users and a listing of more than 400 million purchasable products.
Next Steps
ViSenze is now researching the potential of the Cloud AutoML suite of machine learning products to improve the training of its models and run a fully managed NoSQL database through Cloud Datastore.
“A NoSQL database service is the only missing piece of our architecture for now, and using Cloud Datastore would enable us to focus almost exclusively on our business,” says Yao. “With Google Cloud Platform, we are ideally positioned to continue providing support to our business team and help them continue expanding into new markets.
“In addition, we can help retailers and consumers to unlock the potential of the web and apps to transform the purchasing experience.”
Google Data Cloud: The Catalyst for Modern App Development and Innovation
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97 zettabytes was the estimated volume of data generated worldwide in 20221. This sort of explosion in data volume is happening in every enterprise. Now imagine being able to access all this data you own from anywhere, at any time, analyze it, and leverage its insights to innovate your products and services? One of the biggest barriers to fulfilling this vision is the complexity inherent in dealing with data trapped across silos in an enterprise. Google Data Cloud offers a unified, open, and intelligent platform for innovation, allowing you to integrate your data on a common architectural platform. Across industries, organizations are able to reimagine their data possibilities in entirely new ways and quickly build applications that delight their customers.
Siloed data: The barrier to speed and innovation
Digital technologies ranging from transaction processing to analytics and AI/ML use data to help enterprises understand their customers better. And the pace of innovation has naturally accelerated as organizations learn, adapt, and race to build the next generation of applications and services to compete for customers and meet their needs. At Next 2022, we made a prediction that the barriers between transactional and analytical workloads will mostly disappear.
Traditionally, data architectures have separated transactional and analytical systems—and that’s for good reason. Transactional databases are optimized for fast reads and writes, and analytical databases are optimized for analyzing and aggregating large data sets. This has siloed enterprise data systems, leaving many IT teams struggling to piece together solutions. The result has been time consuming, expensive, and complicated fixes to support intelligent, data-driven applications.
But, with the introduction of new technologies, a more time-efficient and cost-effective approach is possible. Customers now expect to see personalized recommendations and tailored experiences from their applications. With hybrid systems that support both transactional and analytical processing on the same data, without impacting performance, these systems now work together to generate timely, actionable insights that can be used to create better experiences and accelerate business outcomes.
According to a 2022 research paper from IDC, unifying data across silos via a data cloud is the foundational capability enterprises need to gain new insights on rapidly changing conditions and to enable operational intelligence. The modern data cloud provides unified, connected, scalable, secure, extensible, and open data, analytics, and AI/ML services. In this platform, everything is connected to everything else.
Why reducing data barriers delivers more value
The primary benefit of a unified data cloud is that it provides an intuitive and timely way to represent data and allows easy access to related data points. By unifying their data, enterprises are able to:
- Ingest data faster – for operational intelligence
- Unify data across silos – for new insights
- Share data with partners – for collaborative problem solving
- Change forecasting models – to understand and prepare for shifting markets
- Iterate with decision-making scenarios – to ensure agile responses
- Train models on historical data – to build smarter applications
As generative AI applications akin to Bard, an early experiment by Google, become available in the workplace, it will be more important than ever for organizations to have a unified data landscape to holistically train and validate their proprietary large language models.
With these benefits enterprises can accelerate their digital transformation in order to thrive in our increasingly complex digital environment. A survey of more than 800 IT leaders indicated that using a data cloud enabled them to significantly improve employee productivity, operational efficiency, innovation, and customer experience, among others.
Build modern apps with a unified and integrated data cloud
Google Cloud technologies and capabilities reduce the friction between transactional and analytical workloads and make it easier for developers to build applications, and to glean real-time insights. Here are a few examples.
- AlloyDB for PostgreSQL, a fully managed PostgreSQL-compatible database, and AlloyDB Omni, the recently launched downloadable edition of AlloyDB, can analyze transactional data in real time. AlloyDB is more than four times faster for transactional workloads and up to a 100 times faster for analytical queries compared to standard PostgreSQL, according to our performance tests. This kind of performance makes AlloyDB the ideal database for hybrid transactional and analytical processing (HTAP) workloads.
- Datastream for BigQuery, a serverless change data capture and replication service, provides simple and easy real time data replication from transactional databases like AlloyDB, PostgreSQL, MySQL and Oracle directly into BigQuery, Google Cloud’s enterprise data warehouse.
- And, query federation with Cloud Spanner, Cloud SQL, and Cloud Bigtable, make data available right from the BigQuery console allowing customers to analyze data in real-time in transactional databases.
Speed up deployments and lower costs
By reducing data barriers, we’re taking a fundamentally different approach that allows organizations to be more innovative, efficient, and customer-focused by providing:
- Built-in industry leading AI and ML that helps organizations not only build improved insights, but also automate core business processes and enable deep ML-driven product innovation.
- Best-in-class flexibility. Integration with open source standards and APIs ensures portability and extensibility to prevent lock-in. Plus, choice of deployment options means easy interoperability with existing solutions and investments.
- The most unified data platform with the ability to manage every stage of the data lifecycle, from running operational databases to managing analytics applications across data warehouses and lakes to rich data-driven experiences.
- Fully managed database services that free up DBA/DevOps time to focus on high-value work that is more profitable to the business. Organizations that switch from self-managed databases eliminate manual work, realize significant cost savings, reduce risk from security breaches and downtime and increase productivity and innovation. In an IDC survey, for example, Cloud SQL customers achieve an average three-year ROI of 246% because of the value and efficiencies this fully managed service delivers.
Google Data Cloud improves the efficiency and productivity of your teams, resulting in increased innovation across your organization. This is the unified, open approach to data-driven transformation bringing unmatched speed, scale, security, and with AI built in.
In case you missed it: Check out our Data Cloud and AI Summit that happened on March 29th, to learn more about the latest innovations across databases, data analytics, BI and AI. In addition, learn more about the value of managed database services like Cloud SQL in this IDC study.
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