As part of the daily business operations on its advertising platform, Twitter serves billions of ad engagement events, each of which potentially affects hundreds of downstream aggregate metrics. To enable its advertisers to measure user engagement and track ad campaign efficiency, Twitter offers a variety of analytics tools, APIs, and dashboards that can aggregate millions of metrics per second in near-real time.

In this post, you’ll get details on how the Twitter Revenue Data Platform engineering team, led by Steve Niemitz, migrated their on-prem architecture to Google Cloud to boost the reliability and accuracy of Twitter’s ad analytics platform.

Deciding to migrate

Over the past decade, Twitter has developed powerful data transformation pipelines to handle the load of its ever-growing user base worldwide. The first deployments for those pipelines were initially all running in Twitter’s own data centers. The input data streamed from various sources into Hadoop Distributed File System (HDFS) as LZO-compressed Thrift files in an Elephant Bird container format. The data was then processed and aggregated in batches by Scalding data transformation pipelines. Then, aggregation results were output into Manhattan, Twitter’s homegrown distributed key-value store, for serving. Additionally, a streaming system using Twitter’s homegrown systems Eventbus (a messaging tool built on top of DistributedLog), Heron (a stream processing engine), and Nighthawk (a sharded Redis deployment) powered the real-time analytics that Twitter had to provide, filling the gap between the current time and the last batch run.

While this system consistently sustained massive scale, its original design and implementation was starting to reach some limits. In particular, some parts of the system that had grown organically over the years were difficult to configure and extend with new features. Some intricate, long-running jobs were also unreliable, leading to sporadic failures. The legacy end-user serving system was very expensive to run and couldn’t support large queries.

To accommodate for the projected growth in user engagement over the next few years and streamline the development of new features, the Twitter Revenue Data Platform engineering team decided to rethink the architecture and deploy a more flexible and scalable system in Google Cloud.

Platform modernization: First iteration

In the middle of 2017, Steve and his team tackled the first redesign iteration of its advertising data platform modernization, leading to Twitter’s collaboration with Google Cloud.

At first, the team left the data aggregation legacy Scalding pipelines unchanged and continued to run them in Twitter’s data centers. But the batch layer’s output was switched from Manhattan to two separate storage locations in Google Cloud:

• BigQuery—Google’s serverless and highly scalable data warehouse, to support ad-hoc and batch queries.
• Cloud Bigtable—Google’s low-latency, fully managed NoSQL database, to serve as a back end for online dashboards and consumer APIs.

The output aggregations from the Scalding pipelines were first transcoded from Hadoop sequence files to Avro on-prem, staged in four-hour batches to Cloud Storage, and then loaded into BigQuery. A simple pipeline deployed on Dataflow, Google Cloud’s fully managed streaming and batch analytics service, then read the data from BigQuery and applied some light transformations. Finally, the Dataflow pipeline wrote the results into Bigtable.

The team built a new query service to fetch aggregated values from Bigtable and process end-user queries. They deployed this query service in a Google Kubernetes Engine (GKE) cluster in the same region as the Bigtable instance to optimize for data access latency.

Here’s a look at the architecture:

This first iteration already brought many important benefits:

• It de-risked the overall migration effort, letting Twitter avoid migrating both the aggregation business logic and storage at the same time.
• The end-user serving system’s performance improved substantially. Thanks to Bigtable’s linear scalability and extremely low latency for data access, the serving system’s P99 latencies decreased from 2+ seconds to 300ms.
• Reliability increased significantly. The team now rarely, if ever, gets paged for the serving system anymore.

Platform modernization: second iteration

With the new serving system in place, in 2019 the Twitter team began to redesign the rest of the data analytics pipeline using Google Cloud technologies. The redesign sought to solve several existing pain points:

• Because the batch and streaming layers ran on different systems, much of the logic was duplicated between systems.
• While the serving system had been moved into the cloud, the existing pain points of the Hadoop aggregation process still existed.
• The real-time layer was expensive to run and required significant operational attention.

With these pain points in mind, the team began evaluating technologies that could help solve them. They considered several open-source stream processing frameworks initially: Apache Flink, Apache Kafka Streams, and Apache Beam. After evaluating all possible options, the team chose Apache Beam for a few key reasons:

• Beam’s built-in support for exactly-once operations at extremely large scale across multiple clusters.
• Deep integration with other Google Cloud products, such as Bigtable, BigQuery, and Pub/Sub, Google Cloud’s fully managed, real-time messaging service.
• Beam’s programming model, which unifies batch and streaming and lets a single job operate on either batch inputs (Cloud Storage), or streaming inputs (Pub/Sub).
• The ability to deploy Beam pipelines on Dataflow’s fully managed service.

The combination of Dataflow’s fully managed approach and Beam’s comprehensive feature set let Twitter simplify the structure of its data transformation pipeline, as well as increase overall data processing capacity and reliability.

Here’s what the architecture looks like after the second iteration:

In this second iteration, the Twitter team re-implemented the batch layer as follows: Data is first staged from on-prem HDFS to Cloud Storage. A batch Dataflow job then regularly loads the data from Cloud Storage, processes the aggregations, and dual-writes the results to BigQuery for ad-hoc analysis and Bigtable for the serving system.

The Twitter team also deployed an entirely new streaming layer in Google Cloud. For data ingestion, an on-prem service now pushes two different streams of Avro-formatted messages to Pub/Sub. Each message contains a bundle of multiple raw events and affects between 100 and 1,000 aggregations. This leads to more than 3 million aggregations per second performed by four Dataflow jobs (J0-3 in the diagram above). All Dataflow jobs share the same topology, although each job consumes messages from different streams or topics.

One stream, which contains critical data, enters the system at a rate of 200,000 messages per second and is partitioned in two separate Pub/Sub topics. A Dataflow job (J3 in the diagram) consumes those two streams, performs 400,000 aggregations per second, and outputs the results to a table in Bigtable.

The other stream, which contains less critical but higher volume data, enters the system at a rate of around 80,000 messages per second and is partitioned into six separate topics. Three Dataflow jobs (J0, J1, and J2) share the processing of this larger stream, with each of them handling two of the available six topics in parallel, then also outputting the results to a table in Bigtable. In total, those three jobs process over 2 million aggregations/second.

Partitioning the high-volume stream into multiple topics offers a number of advantages:

• The partitioning is organized by applying a hash function on the aggregation key and then dividing the function’s result by the number of available partitions (in this case, six). This guarantees that any per-key grouping operation in downstream pipelines is scoped to a single partition, which is required for consistent aggregation results.
• When deploying updates to the Dataflow jobs, admins can drain and relaunch each job individually in sequence, allowing the remaining pipelines to continue uninterrupted and minimizing impact on the end users.
• The three jobs can each handle two topics without issue currently, and there is still room to scale horizontally up to six jobs if needed. The number of topics (six) is arbitrary, but is a good balance at the moment based on current needs and potential spikes in traffic.

To assist with job configuration, Twitter initially considered using Dataflow’s template system, a powerful feature that enables the encapsulation of Dataflow pipelines into repeatable templates that can be configured at runtime. However, since Twitter needed to deploy jobs with topologies that might change over time, the team decided instead to implement a custom declarative system where developers can specify different parameters for their jobs in a pystachio DSL: tuning parameters, data sources to operate on, sink tables for aggregation outputs, and the jobs’ source code location. A new major version of Dataflow templates, called Flex Templates, will remove some of the previous limitations with the template architecture and allow any Dataflow job to be templatized.

For job orchestration, the Twitter team built a custom command line tool that processes the configuration files to call the Dataflow API and submit jobs. The tool also allows developers to submit a job update by automatically performing a multi-step process, like this:

1. Drain the old job:
   • Call the Dataflow API to identify which data sources are used in the job (for example, a Pub/Sub topic reader).
   • Initiate a drain request.
   • Poll the Dataflow API for the watermark of the identified sources until the maximum watermark is hit, which indicates that the draining operation is complete.
2. Launch the new job with the updated code.

This simple, flexible, and powerful system allows developers to focus on their data transformation code without having to be concerned about job orchestration or the underlying infrastructure details.

Looking ahead

Six months after fully transitioning its ad analytics data platform to Google Cloud, Twitter has already seen huge benefits. Twitter’s developers have gained in agility as they can more easily configure existing data pipelines and build new features much faster. The real-time data pipeline has also greatly improved its reliability and accuracy, thanks to Beam’s exactly-once semantics and the increased processing speed and ingestion capacity enabled by Pub/Sub, Dataflow, and Bigtable.

Twitter engineers have enjoyed working with Dataflow and Beam for several years now, since version 2.2, and plan to continue expanding their usage. Most importantly, they’ll soon merge the batch and streaming layers into a single, authoritative streaming layer.

Throughout this project, the Twitter team collaborated very closely with Google engineers to exchange feedback and discuss product enhancements. We look forward to continuing this joint technical effort on several ongoing large-scale cloud migration projects at Twitter. Stay tuned for more updates!

Research shows that over 90% of large organizations already deploy multicloud architectures, and their data is distributed across several public cloud providers. Additionally, data is also increasingly split across various storage systems such as warehouses, operational and relational databases, object stores, etc. With the proliferation of new applications, data is serving many more use cases such as data sciences, business intelligence, analytics, streaming and the list goes on. With these data trends, customers are increasingly gravitating towards an open multicloud data lake. However, multicloud data lakes present several challenges such as data silos, data duplication, fragmented governance, complexity of tools, and increased costs.

With Google’s data cloud technologies, customers can leverage the unique combination of distributed cloud services. They can create an agile cross-cloud semantic business layer with Looker and manage data lakes and data warehouses across cloud environments at scale with BigQuery and capabilities like BigLake and BigQuery Omni.

BigLake is a storage engine that unifies data warehouses and lake houses by standardizing across different storage formats including BigQuery managed table and open file formats such as Parquet and Apache Iceberg on object storage. BigQuery Omni provides the compute engine that runs locally to the storage on AWS or Azure, which customers can use to query data in AWS or Azure seamlessly. This provides several key benefits such as:

1. A single pane of glass to query your multicloud data lakes (across Google Cloud Platform, Amazon Web Services, and Microsoft Azure)
2. Cross-cloud analytics by combining data across different platforms with little to no egress costs
3. Unified governance and secure management of your data wherever it resides

In this blog, we will share cross-cloud analytics use cases customers are solving with Google’s Data Cloud and the benefits they are realizing.

Unified marketing analytics for 360-degree insights

Organizations want to perform marketing analytics – ads optimization, inventory management, churn prediction, buyer propensity trends and many more such analytics. To do this before BigQuery Omni, customers had to use data from several different sources such as Google Analytics, public datasets and other proprietary information stored across cloud environments. This requires moving large amounts of data, managing duplicate copies and incremental costs to perform any cross-cloud analytics and derive actionable insights. With BigQuery Omni, organizations are able to greatly simplify this workflow. Using the familiar BigQuery interface, users can access data residing in AWS or Azure, discover and select just the relevant data that needs to be combined for further analysis. This subset of data can be moved to Google Cloud using Omni’s new Cross-Cloud Transfer capabilities. Customers can combine this data with other Google Cloud datasets and these consolidated tables can be made available to key business stakeholders through advanced analytics tools such as Looker and Looker Studio. Customers are also able to tie in this data now with world class AI models via Vertex AI.

As an illustrative example, consider a retailer who has sales & inventory, user and search data spread across multiple data silos. Using BigQuery Omni they can seamlessly bring these datasets together and power several marketing analytics scenarios like customer segmentation, campaign management and demand forecasting etc.

“Interested in performing cross-cloud analytics, we tested BigQuery Omni and really liked the SQL support to easily get data from AWS S3. We have seen great potential and value in BigQuery Omni for adopting a multi-cloud data strategy.” — Florian Valeye, Staff Data Engineer, Back Market, a leading online marketplace for renewed technology based out of France

Data platform with consistent and unified cross-cloud governance

Another pattern is customers looking to analyze operational, transactional and business data across data silos in different clouds through a unified data platform. These data silos are a result of various factors such as merger and acquisitions, standardization of analytical tools, leveraging best of breed solutions in different clouds and diversification of data footprint across clouds. In addition to a single pane of glass for data access across silos, customers deeply desire consistent and uniform governance of their data across clouds.

“Achieve is looking to deliver a consistent analytics experience to all our customers and stakeholders. With our financial and credit report data distributed across clouds, accessing and getting insights holistically is difficult. Through our exploration with Omni, we are able to access datasets in different clouds using a single familiar BigQuery interface; we see its promise as one of the primary tools in our multi-cloud platform.” — James Simonson, Senior Data Engineer, Achieve

With BigLake and BigQuery Omni abstracting the storage and compute layers respectively, organizations can access and query their data in Google Cloud irrespective of where it resides. They can also set fine-grained row level and column access policies in BigQuery and consistently govern it across clouds. These building blocks enable data engineering teams to build a unified and governed data platform for their data users without having to deal with the complexity of building and managing complex data pipelines. Furthermore, with BigQuery Omni’s integration with Dataplex and Data Catalog, you can discover, search your data across clouds and enrich your data by adding relevant business context with business glossary and rich text.

“Several SADA customers use GCP to build and manage their data analytics platform. During many explorations and proofs of concepts, our customers have seen the great potential and value in BigQuery Omni. Enabling seamless cross-cloud data analytics has allowed them to realize the value of their data quicker while lowering the barrier to entry for BigQuery adoption in a low-risk fashion.” — Brian Suk, Associate Chief Technology Officer, SADA, one of the strategic partners of Google Cloud.

Simplified data sharing between data providers and their customers

A third emerging pattern in cross cloud analytics is data sharing. Several services have the business need to share information such as inventory data, subscriber data to their customers or users who in turn analyze or aggregate the data with their proprietary data and oftentimes share the results back with the service provider. In several cases, the two parties are on different cloud environments, requiring them to move data back and forth.

Consider a company operating in the customer data platform (CDP) space. CDPs were designed to help activate customer data, and a critical first step of that was unifying and managing that customer data. To enable this, many CDP vendors built their solution choosing one of the available cloud infrastructure technologies and copied data from the client’s systems.“Copying data from client applications and infrastructure has always been a requirement to deploy a CDP, but it doesn’t have to be anymore” — Justin DeBrabant, Senior Vice President of Product, ActionIQ.

While a small percentage of customers are fine with moving data across cloud environments, the majority are hesitant to onboard new services and would rather prefer providing governed access to their data sets.

“A new architectural pattern is emerging, allowing organizations to keep their data at one location and make it accessible, with the proper guardrails, to applications used by the rest of the organization’s stack” adds Justin at ActionIQ.

With BigQuery Omni, services in Google Cloud Platform can more easily access and share data with their customers and users in other cloud environments with limited data movement. One of UK’s largest statistics providers has explored Omni for their data sharing needs.

“We tested BigQuery Omni and really like the ability to get data from AWS directly into BQ. We’re excited about managing data sharing with different organizations without onboarding new clouds” – Simon Sandford-Taylor, Chief Information and Digital Officer, UK’s Office for National Statistics

With BigQuery Omni, customers are able to:

• Access and query data across clouds through a single user interface
• Reduce the need for data engineering before analyzing data
• Lower operational overhead and risks by deploying an application that runs across multiple clouds which leverages the same, consistent security controls
• Accelerate access to insights by significantly reducing the time for data processing and analysis
• Create consistent and predictable budgeting across multiple cloud footprints
• Enable long term agility and maximize the benefits every cloud investment

Over the last year, we’ve seen great momentum in customer adoption and added significant innovations to BigQuery Omni including improved performance and scalability for querying your data in AWS S3 or Azure Blob Storage, Iceberg support for Omni, Larger query result set size up to 20GB and Cross-cloud transfer that helps customers easily, securely, and cost effectively move just enough data across cloud environments for advanced analytics.

BigQuery Omni has launched several features to support unified governance of your data across multiple clouds – you can get fine-grained access to your multi-cloud data with row level and column level security. Building on this, we are excited to announce that BigQuery Omni now supports data masking. We’ve also made it easy for customers to try and see the benefits of BigQuery Omni through the limited time free trial available until March 30, 2023.

BigQuery Omni running on other public clouds outside of Google Cloud is available in AWS US East1 (N.Virginia) and Azure US East2 (US East) regions. We are also excited to share that we will be bringing BigQuery Omni to more regions in the future, starting with Asia Pacific (AWS Korea) coming soon.

Getting Started

Get started with a free trial to learn about Omni. Check out the documentation to learn more about BigQuery Omni. You can also leverage the self paced labs to learn how to set up BigQuery Omni easily.

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: 

1. Ingest data faster – for operational intelligence
2. Unify data across silos – for new insights
3. Share data with partners – for collaborative problem solving
4. Change forecasting models – to understand and prepare for shifting markets
5. Iterate with decision-making scenarios – to ensure agile responses
6. 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. 

1. 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.
2. 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.
3. 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.

^{1. How Big is Big? – Statista}

Over one third of organizations believe that data analytics and machine learning have the most potential to significantly alter the way they run business over the next 3 to 5 years. However, only 26% of organizations are data driven. One of the biggest reasons for this gap is that a major portion of the data generated today is unstructured, which includes images, documents, and videos. It is estimated to cover roughly up to 80% of all data, which has so far remained untapped by organizations.

One of the goals of Google’s data cloud is to help customers realize value from data of all types and formats. Earlier this year, we announced BigLake, which unifies data lakes and warehouses under a single management framework, enabling you to analyze, search, secure, govern and share unstructured data using BigQuery.

At Next ‘22, we announced the preview of object tables, a new table type in BigQuery that provides a structured record interface for unstructured data stored in Google Cloud Storage. This enables you to directly run analytics and machine learning on images, audio, documents and other file types using existing frameworks like SQL and remote functions natively in BigQuery itself. Object tables also extend our best practices of securing, sharing and governing structured data to unstructured, without needing to learn or deploy new tools.

Directly process unstructured data using BigQuery ML

Object tables contain metadata such as URI (Uniform Resource Identifier), content type, and size that can be queried just like other BigQuery tables. You can then derive inferences using machine learning models on unstructured data with BigQuery ML. As part of preview, you can import open source TensorFlow Hub image models, or your own custom models to annotate the images. Very soon, we plan to enable this for audio, video, text and many other formats, and pre-trained models to enable out-of-the box analysis. Check out this video to learn more and watch a demo.

Create an object table

CREATE EXTERNAL TABLE my_dataset.object_table
WITH CONNECTION us.my_connection
OPTIONS(uris=["gs://mybucket/images/*.jpg"],
object_metadata="SIMPLE", metadata_cache_mode="AUTOMATIC");
​
# Generate inferences with BQML
SELECT * FROM ML.PREDICT(
MODEL my_dataset.vision_model,
(SELECT ML.DECODE_IMAGE(data) AS img FROM my_dataset.object_table)
);

By analyzing unstructured data natively in BigQuery, businesses can

• Eliminate manual effort as pre-processing steps such as tuning image sizes to model requirements are automated
• Leverage the simple and familiar SQL interface to quickly gain insights
• Save costs by utilizing existing BigQuery slots without needing to provision new forms of compute

Adswerve is a leading Google Marketing, Analytics and Cloud partner on a mission to humanize data. Twiddy & Co. is Adswerve’s client – a vacation rental company in North Carolina. By combining structured and unstructured data, Twiddy and Adswerve used BigQuery ML to analyze images of rental listings and predict the click-through rate, enabling data-driven photo editorial decisions.

“Twiddy now has the capability to use advanced image analysis to stay competitive in an ever changing landscape of vacation rental providers – and can do this using their in-house SQL skills.” said Pat Grady, Technology Evangelist, Adswerve

Process unstructured data using remote functions

Customers today use remote functions (UDFs) to process structured data for languages and libraries that are not supported in BigQuery. We are extending this capability to process unstructured data using object tables.

Object tables provide signed URLs to allow remote UDFs running on Cloud Functions or Cloud Run to process the object table content. This is particularly useful for running Google’s pre-trained AI models, including Vision AI, Speech-to-Text, Document AI, open source libraries such as Apache Tika, or deploying your own custom models where performance SLAs are important.

Here’s an example of an object table being created over PDF files that are parsed using an open source library running as a remote UDF.

SELECT uri, extract_title(samples.parse_tika(signed_url)) AS title<br>FROM EXTERNAL_OBJECT_TRANSFORM(TABLE pdf_files_object_table,<br>["SIGNED_URL"]);

Extending more BigQuery capabilities to unstructured data

Business intelligence – The results of analyzing unstructured data either directly in BigQuery ML or via UDFs can be combined with your structured data to build unified reports using Looker Studio (at no charge), Looker or any of your preferred BI solutions. This allows you to gain more comprehensive business insights. For example, online retailers can analyze product return rates by correlating them with the images of defective products. Similarly, digital advertisers can correlate ad performance with various attributes of ad creatives to make more informed decisions.

BigQuery search index – Customers are increasingly using the search functionality of BigQuery to power search use cases. These capabilities now extend to unstructured data analytics as well. Whether you use BigQueryML to produce inference on images or use remote UDFs with Doc AI to produce document extraction, the results can now be search indexed and used to support search access patterns.

Here’s an example of search index on data that is parsed from PDF files:

CREATE SEARCH INDEX my_index ON pdf_text_extract(ALL COLUMNS);
​
SELECT * FROM pdf_text_extract WHERE SEARCH(pdf_text, "Google");

Security and governance – We are extending BigQuery’s row-level security capabilities to help you secure objects in Google Cloud Storage. By securing specific rows in an object table, you can restrict the ability of end users to retrieve the signed URLs of corresponding URIs present in the table. This is a shared responsibility security model, for which administrators need to ensure that end users don’t have direct access to Google Cloud Storage, and use signed URLs from object tables as the only access mechanism.

Here’s an example of a policy for PII images that are secured to be first processed through a blur pipeline:

CREATE ROW ACCESS POLICY pii_data ON object_table_images
GRANT TO ("group:admin@example.com")
FILTER USING (ARRAY_LENGTH(metadata)=1 AND
metadata[OFFSET(0)].name="face_detected")

Soon, Dataplex will support object tables, allowing you to automatically create object tables in BigQuery and manage and govern unstructured data at scale.

Data sharing – You can now use Analytics Hub to share unstructured data with partners, customers and suppliers while not compromising on security and governance. Subscribers can consume the rows of object tables that are shared with them, and use signed URLs for unstructured data objects.

Getting Started

Submit this form to try these new capabilities that unlock the power of your unstructured data in BigQuery. Watch this demo to learn more about these new capabilities.

Special thanks to engineering leaders Amir Hormati, Justin Levandoski and Yuri Volobuev for contributing to this post.

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.”