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How BigQuery’s Unique Features Support Your Data at Petabyte-scale

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BigQuery's design enables large scale data analysis to unveil valuable insights and allows data can be ingested into BigQuery through batch or stream data directly. You can learn to leverage BigQuery to analyze data up to petabyte-scale.

Organizations rely on data warehouses to aggregate data from disparate sources, process it, and make it available for data analysis in support of strategic decision-making. BigQuery is the Google Cloud enterprise data warehouse designed to help organizations to run large scale analytics with ease and quickly unlock actionable insights. You can ingest data into BigQuery either through batch uploading or by streaming data directly to unlock real-time insights. As a fully-managed data warehouse, BigQuery takes care of the infrastructure so you can focus on analyzing your data up to petabyte-scale. BigQuery supports SQL (Structured Query Language), which you’re likely already familiar with if you’ve worked with ANSI-compliant relational databases. 

BQ sketch
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BigQuery unique features

BI Engine – BigQuery BI Engine is a fast, in-memory analysis service that provides subsecond query response times with high concurrency. BI Engine integrates with Google Data Studio and Looker for visualizing query results and enables integration with other popular business intelligence (BI) tools. 

BigQuery ML: BigQuery ML is unlocking machine learning for millions of data analysts. It  enables data analysts and data scientists to build and operationalize machine learning models directly within BigQuery, using simple SQL.

Unique BQ Features
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BigQuery Omni – BigQuery Omni is a flexible, multi-cloud analytics solution powered by Anthos that lets you cost-effectively access and securely analyze data across Google Cloud, Amazon Web Services (AWS), and Azure, without leaving the BigQuery user interface (UI). Using standard SQL and familiar BigQuery APIs, you can break down data silos and gain critical business insights from a single pane of glass. 

Data QnA: Data QnA enables self-service analytics for business users on BigQuery data as well as federated data from Cloud Storage, Bigtable, Cloud SQL, or Google Drive. It uses Dialogflow and enables users to formulate free-form text analytical questions, with auto-suggested entities while users type a question.

Connected Sheets -The native integration between Sheets and BigQuery makes it possible for all business stakeholders, who are already quite familiar with spreadsheet tools, to get their own up-to-date insights at any time.

Geospatial data – BigQuery offers accurate and scalable geospatial analysis with geography data types. It supports core GIS functions – measurements, transforms, constructors, and more – using standard SQL.

How does it work?

how it works BQ

Here’s how it works: You ingest your own data into BigQuery or use data from the public datasets. Storage and compute are decoupled and can scale independently on demand. This offers immense flexibility and cost control for your business as you don’t need to keep expensive compute resources up and running all the time. As a result, BigQuery is much more cost-effective than traditional node-based cloud data warehouse solutions or on-premises systems. BigQuery also provides automatic backup and restore of your data. 

You can ingest data into BigQuery in batches or stream real-time data from web, IoT, or mobile devices via Pub/Sub. You can also use Data Transfer Service to ingest data from other clouds, on-premises systems or third-party services. BigQuery also supports ODBC and JDBC drivers to connect with existing tools and infrastructure. 

Interacting with BigQuery to load data, run queries, or create ML models can be done in three different ways. You can use the UI in the Cloud Console, the BigQuery command-line tool, or the API via client libraries available in several languages.

When it comes time to visualize your data, BigQuery integrates with Looker as well as several other business intelligence tools across the Google partner ecosystem.

What about security?

BigQuery offers built-in data protection at scale. It provides security and governance tools to efficiently govern data and democratize insights within your organization. 

  • Within BigQuery, users can assign dataset-level and project-level permissions to help govern data access. Secure data sharing ensures you can collaborate and operate your business with trust.
  • Data is automatically encrypted both while in transit and at rest, ensuring that your data is protected from intrusions, theft, and attacks. 
  • Cloud DLP helps you discover and classify sensitive data assets.
  • Cloud IAM provides access control and visibility into security policies.
  • Data Catalog helps you discover and manage data. 

How much does it cost?

bq cost

The BigQuery sandbox lets you explore BigQuery capabilities at no cost and confirm that BigQuery fits your needs. With BigQuery you get predictable price-performance: you pay for storing and querying data, and for streaming inserts. Loading and exporting data are free of charge. Storage costs are based on the amount of data stored, and have two rates based on how often the data is changing. Query costs can be either:

  • On-demand – you are charged per query by the amount of data processed
  • Flat-rate – if you prefer to purchase dedicated resources 

You can start with the pay-as-you-go, on-demand option and later move to flat-rate if that better suits your usage. Or, start with flat-rate, get a better understanding of your usage and move to the pay-as-you-go models for additional workloads. 

To explore BigQuery and its capabilities a bit more, check out the sandbox; and when you’re ready to modernize your data warehouse with BigQuery then check out the documentation to streamline your migration process here. https://www.youtube.com/embed/So-tVyBQt8E?enablejsapi=1&

For more #GCPSketchnote, follow the GitHub repo. For similar cloud content follow me on Twitter @pvergadia and keep an eye out on thecloudgirl.dev.

Case Study

Mrs. T’s Pierogies Moves SAP Systems to Google Cloud for Faster Analytics Capabilities for its SAP Data

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Leading manufacturer of frozen Pierogies, Mrs. T’s Pierogies seamlessly migrated its legacy on-prem infrastructure to SAP S/4HANA solution on Google Cloud with minimal downtime. This created fast and flexible analytical capabilities for SAP data!

Pierogies might just be the ultimate comfort food. But when Mrs. T’s Pierogies — the leading manufacturer of frozen pierogies in the US — learned it needed to transition its existing on-premises SAP ECC to S/4HANA, the company sought a little comfort for itself. 

Founded in 1952, Mrs. T’s Pierogies now produces more than 650 million pierogies a year. Moving that many pierogies requires a powerful ERP system — and an equally powerful IT infrastructure on which to run it. Mrs. T’s realized that the SAP-mandated transition of its ERP solution to SAP S/4HANA was an opportunity to move its SAP systems to Google Cloud and gain real-time analytics capabilities for faster sales forecasting, more effective trade promotions, and more sophisticated planning.

From necessity to opportunity

Mrs. T’s successfully ran its SAP ECC solution on its own on-premises servers for years. But after SAP decided to sunset the product, Mrs. T’s realized that it faced multiple challenges, including: 

  • Migrating its SAP ECC 6.0 system from an on-premises leagcy OS to a cloud-based Linux environment
  • Moving data from its SAP DB2 database to HANA
  • Transitioning from ECC to SAP S/4HANA

That complex transition needed to take place with little or no downtime, since nearly all of the company’s invoices, warehouse movements, and transfer orders used the Electronic Data Interchange (EDI) protocol. Missing even a few hours of EDI transactions would put significant revenue at stake. Adding to the challenge: Some Mrs. T’s customers would not accept an invoice past five days, which left little room for error. Mrs. T’s chose Rackspace Technology, a longtime Google Cloud partner, to oversee the move. 

Mrs. T’s could have chosen to run S/4HANA on its legacy hardware but saw migration to Google Cloud as an opportunity to improve key aspects of its business, in particular data analytics. Historically, sales planning and forecasting involved time-consuming manual processes. But the speed, availability, and scalability of Google Cloud meant that Mrs. T’s could take advantage of S/4HANA’s embedded analytics capabilities. Migrating could also open the door to leveraging Google Cloud’s native integration of SAP data to power Google tools such as BigQueryGoogle Cloud AI Building Blocks, and more. 

The move to Google Cloud also gave Mrs. T’s an opportunity to update its disaster recovery process. Previously, the company backed up to tape. So, if its SAP systems went down, a member of the IT department would have to drive the most recent tape backups an hour to its cold site, where the disaster recovery partner would load the SAP backup tape, boot the system up, switch network connections to that site, and cross their fingers. Not only would downtime be significant, but restored data would be limited to the periodic tape backup. 

“Everything just worked”

Once Mrs. T’s decided to migrate to Google Cloud, the company worked with Rackspace Technology to implement the system. The first phase focused on moving the SAP production environment from on-premises infrastructure to Google Cloud and updating its database to HANA, which took place over 12 weeks. The switchover occurred over a weekend and was all but invisible to users. “We came in on Monday and everything just worked,” recalls Timothy Coyle, Director of Information Systems & Technology. In a second four-month phase, the company transitioned from ECC to S/4. 

The move to Google Cloud paid dividends immediately. Batch transactions occurred twice as fast and on-screen end-user transactions rendered instantly. The upgrade also gave the finance team access to embedded analytics and monitoring for the first time. With everything now in the cloud, disaster recovery could be dynamic and nearly instantaneous, with a worst-case scenario of just 5 to 10 minutes of downtime. 

“Mrs. T’s needed a skilled and experienced partner that could move its SAP environment to Google Cloud with no negative impacts to its business. We knew this migration was a key initiative in Mrs. T’s digital transformation journey,” says Chuck Britton, Google Partner Development Manager at Rackspace. “We also knew that running SAP on Google Cloud would give the business the fast and flexible analytical capabilities it needed for its SAP data.” 

From ideation to production, Mrs. T’s migrated from its legacy on-premises infrastructure to a modern SAP S/4HANA solution on Google Cloud in only seven months, with minimal downtime and zero disruptions. Now that the migration is complete, Mrs. T’s has a flexible, highly scalable environment to run the SAP applications and data that fuel the business. Says Coyle, “Our strategic intent for IT is to make processes simpler, people more productive, and infrastructure more secure. This project fits right square in the middle of that strategic philosophy.”

Learn more about ways in which Google Cloud can transform your SAP experience and about Rackspace Google Cloud solutions for SAP customers.

Research Reports

Report on API-led Digital Transformations in 2020 and the Future

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Read the State of API Economy 2021 report to gain insights on API-led digital economy and in-depth analysis of Google Cloud's Apigee API Management Platform usage data, case studies and third-party surveys conducted with tech leaders.

In 2020, many businesses across industries turned their focus and investments towards digital strategies. APIs being an integral part of every organization’s digital disruption, will grow in relevance throughout 2021. Read the report to gain more insights on driving API-led digital transformations and in-depth analysis of Google Cloud’s Apigee API Management Platform usage data, case studies and third-party surveys conducted with tech leaders.

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Explainer

GCP for Bioinformatics

Join Cloud GDE and developer Lynn Langit in this fast-paced session to get resources you can use to learn how to use the Google Cloud Platform for bioinformatics.

Lynn has created an open source course (on GitHub) to introduce researchers to using GCP to scale their analysis jobs. In this short talk, she’ll guide you through her course materials, so that you can get started learning using examples from genomics.

She talks is divided into four parts:

  • What is needed?
  • Why do we need to have patterns?
  • How can you use pattern information,
  • How can you learn more.

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Case Study

How Major League Baseball Migrated from Teradata to BigQuery

Ever wondered how to jumpstart a migration from your legacy on-premises or cloud data warehouse to BigQuery?

In this video Robert Goretsky, VP, Data Engineering, Major League Baseball and Ryan McDowell, Cloud Data Engineer, Google Cloud show you how.

They offer a deep-dive into MLB’s Teradata to BigQuery migration journey. In the course of the session they share the lessons they learnt, and business insights that they achieved as a result of modernizing MLB’s analytics strategy.

Find out how to run a migration assessment, migrate a subset of the data and schema with BigQuery DTS, and transform the SQL queries from Teradata into BigQuery dialect with CompilerWorks. Watch this video to get a deep understanding of your enterprise data warehouse migration journey.

Case Study

“It’s an Astonishing Difference”: What Data Operation Execs say About Google Cloud’s Data Warehouse

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When Blue Apron had issues running its data warehouse on another cloud provider, it built an analytics platform using Looker and Google BigQuery to enable faster business decisions about food inventory.

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.”

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