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.

Blue Apron’s mission is to make incredible home cooking accessible to everyone. Launched in 2012, Blue Apron is reimagining the way that food is produced, distributed, and consumed.

Industries: Retail & Consumer Goods

Location: United States

Google Cloud Results

• Enables near real-time business decisions to better manage food inventory and delivery by reducing query times exponentially
• Helps improve customer service and optimize business processes with faster insights
• Reduces costs while reclaiming up to a week per month of engineering

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.

A Google Cloud partner and winner of the 2016 Google Cloud Global Partner Award for Solution Innovation, Looker integrates its solution with Google Cloud Platform to help customers modernize their analytics.

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.”

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.

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 Google Workspace 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.”

Need a quick overview of Google Cloud analytics technologies? Quickly learn these 11 Google Cloud products—each explained in under two minutes.

BigQuery in a minute

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Firestore in a minute

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Cloud SQL in a minute

Cloud SQL is a fully-managed database service that helps you set up, maintain, manage, and administer your relational databases on Google Cloud. In this video you’ll learn how Cloud SQL can help you with time-consuming tasks such as patches updates, replicas, and backups so you can focus on designing your application.

Memorystore in a minute

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BigQuery ML lets you create and execute machine learning models in BigQuery by using standard SQL queries. In this video, learn how you can use BigQuery ML for your machine learning projects.

Dataflow in a minute

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Cloud Pub/Sub in a minute

Cloud Pub/Sub is an asynchronous messaging service that decouples services that produce events from services that process events. In this video, you’ll learn how you can use it for message storage, real-time message delivery, and much more, while still providing consistent performance at scale and high availability.

Dataproc in a minute

Dataproc is a managed service that lets you take advantage of open source data tools like Apache Spark, Flink and Presto for batch processing, SQL, streaming, and machine learning. In this video, you’ll learn what Dataproc is and how you can use it to simplify data and analytics processing.

Data Fusion in a minute

Cloud Data Fusion is a fully managed, cloud-native, enterprise data integration service for quickly building and managing data pipelines. In this video, you’ll learn how Cloud Data Fusion can help you build smarter data marts, data lakes, and data warehouses.

For a business to succeed in the long term, it needs to learn not just to adapt to inevitable change, but to harness it. South Africa-based PPS has been an insurance company since 1941 and today is the biggest mutual insurance provider in the country.

As a mutual company, PPS is owned by more than 200,000 members, making them shareholders. In recent years, PPS and other companies like it have been affected by a number of external factors.

“For one thing, technology platforms have brought in a new gig economy that has all kinds of implications for insurance,” says Avsharn Bachoo, CTO at PPS. “What we’ve been seeing is basically a disruption of the South African insurance industry. We chose to see that as an opportunity.”

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely. To embrace the world of AI and machine learning effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

—Avsharn Bachoo, CTO, PPS

In early 2018, faced with an uncertain economic environment that was squeezing growth and profitability, PPS decided to transform itself from a traditional broker-based business into a digital insurance provider. A key pillar of this new strategy was to overhaul the company’s technology infrastructure. To turn the strategy into reality, Avsharn and his team chose Google Cloud Platform (GCP).

“Our servers were at the end of their life cycle and we had to decide whether to refresh them or switch completely,” says Avsharn. “To embrace the world of AI and machine learning (ML) effectively, we knew we needed a cloud-based infrastructure. We’ve found the answer in Google Cloud Platform.”

Power, speed, flexibility with Google Cloud Platform

Previously, PPS maintained an on-premises IT infrastructure, which worked for its traditional business but was unsuited for its new way of working. In early 2018, the company started working on new products for its members but this required large amounts of compute power that proved prohibitively expensive with on-premises servers. Even existing products were starting to require more than the infrastructure could deliver. Aging equipment meant that it’s testing and quality assurance environments bore little resemblance to the actual production environment.

“We had no pre-production environments at all,” says Avsharn, resulting in more work for developers after products had been released. Meanwhile, the capital required to buy and configure more servers for new projects meant fewer resources available for innovation, and left the company less able to react to changes in the market. PPS knew it had to find a cloud-based alternative.

Shortly after devising a new digital strategy, PPS engineers attended a training session on cloud infrastructure given by leading South African Google Cloud Partner Siatik. Impressed with the presentation, PPS engaged Siatik to help run a proof of concept for a cloud-based infrastructure, running on GCP. With on-site engineers and constant communication, Siatik formed a very close working relationship with PPS. “The team at Siatik was exemplary,” recalls Avsharn. “They were well-organized, with cutting-edge technical acumen and very creative solutions to our problems. They were real game-changers.”

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information. Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

—Kimoon Kim, Lead Solution Architect and Data Engineer, Siatik

The proof of concept was successful, with GCP outperforming the existing infrastructure in terms of how it handled compute demands, databases, and storage.

“It’s the speed of GCP that really impresses us,” says Avsharn. PPS saw that GCP wasn’t just an opportunity to migrate its existing infrastructure to the cloud. With Siatik’s help, it redesigned its monolithic core architecture to one based around microservices using Google Kubernetes Engine (GKE). For data processing and storage, Cloud Dataflow and Cloud Datastore proved invaluable, while Stackdriver helped the IT team stay on top of logging and monitoring the system.

“Google Cloud makes migrations very easy,” says Brett St. Clair, CEO at Siatik. “It takes care of all the hard work with configurations and replications, so when we switch the machines on, everything is ready and working.”

The ease with which PPS migrated to GCP means that it can now tackle strategic goals much more quickly than before. The most ambitious of these is an AI-powered product recommendation platform. Information is collected from customers who opt in at a defined point in their journey, this database is queried using BigQuery, and the information is fed into the platform. The AI model then calculates the most appropriate products for each member, according to their personal history.

“Most of the product recommendation engines out there are based on clustering, where you’re offered products based on your peer groups,” explains Avsharn. “For the first time, we can make recommendations to members based on their individual preferences and historical behavior. That’s really powerful for us.”

Siatik helped PPS use TensorFlow and Cloud Machine Learning Engine to build the AI platform. For the engineers, these easy-to-use tools helped speed up the process considerably, allowing them to host the models locally without any fuss. Previously, it took one to three months to manually build the model and match an offer to a customer. With the AI platform, a match takes just a few minutes. Cloud ML Engine, in particular, helped the platform adapt to new information on the fly and easily make adjustments to its hyperparameters, that is, preset variables which define the model-training process.

“We wanted the platform to retrain its models in response to new data and improve its recommendations with more information,” says Kimoon Kim, Lead Solution Architect and Data Engineer at Siatik. “Normally this would be a manual process but Google Cloud ML Engine lets the models do this automatically.”

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI. We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”

—Avsharn Bachoo, CTO, PPS

Harnessing artificial intelligence for real-world results

PPS deployed its new AI recommendation platform in December, 2018. Just a couple of months later, its impact was clear. “In around eight weeks, we saw a 5 percent growth in sales,” says Avsharn. “It’s been a direct result of building our recommendation platform with Google Cloud. We can offer the right products to the right members.”

For developers and engineers at PPS, working with Google Cloud gives them access to high performance technology and automation options with GKE. As a result, the infrastructure runs 70 percent faster than before with fewer cores and less memory. Developers can also work in mature testing environments, and for the first time, are able to build pre-production environments, leading to better quality products. More strategically, moving to a serverless, cloud-based infrastructure has helped PPS take control of its budget, moving away from intermittent, large capital spends to more manageable, project-to-project flows of operational expenditure. The company expects to see savings of around 50 percent, or $695,000.

“We have a lot more flexibility with our resources thanks to Google Cloud,” says Avsharn. “When we have a new idea, we don’t have to outlay new capital such as servers before we can even start working on it. We just spin up instances when we want and spin them back down when we’re done.”

With the AI platform deployed and working well, PPS is already looking at ways to improve it, including real-time updates and further automation. Soon, the company will integrate the platform with more sales campaigns for more effective targeting to boost sales even further. Meanwhile, it’s also experimenting with machine learning to spot patterns in data at scale for fraud analytics and risk assessment.

For PPS, working with Google Cloud has helped it transform quickly and effectively from disrupted to disruptor. The company is now looking to gain the same transformative effects by implementing G Suite for increased productivity and collaboration.

“Google Cloud helped us cancel out a lot of the noise around machine learning and AI,” says Avsharn. “We don’t have to build new complicated algorithms or hire huge teams of data scientists to benefit. We just bring our data and use the right tools to focus on what’s really important.”

Streaming analytics helps businesses to understand their customers in real time and adjust their offerings and actions to better serve customer needs. It’s an important part of modern data analytics, and can open up possibilities for faster decisions.

For streaming analytics projects to be successful, the tools have to be easy to use, familiar, and cost-effective. Since its launch in 2015, Cloud Dataflow, Google’s fully managed streaming analytics service, has been known for its powerful API available in Java and Python. Businesses have almost unlimited customization capabilities for their streaming pipelines when they use these two languages, but these capabilities come at the cost of needing programming skills that are sometimes hard to find.

To advance Google Cloud’s streaming analytics further, Google Cloud is announcing new features available in the public preview for Cloud Dataflow SQL, as well as the general availability of Cloud Dataflow Flexible Resource Scheduling (FlexRS) for a very cost-effective way of batch processing of events. These new features make streaming analytics easier and more accessible to data engineers, particularly those with database experience. 

Here’s more detail about each of these new features:

Cloud Dataflow SQL to create streaming (and batch) pipelines using SQL
We know that SQL is the standard for defining data transformations, and that you want more GUI-based tools for creating pipelines. With that in mind, several months ago we launched a public preview of Cloud Dataflow SQL, an easy way to use SQL queries to develop and run Cloud Dataflow jobs from the BigQuery web UI. Today, we are launching several new features in Cloud Dataflow SQL, including Cloud Storage file support and a visual schema editor.

Cloud Dataflow SQL allows you to join Cloud Pub/Sub streams with BigQuery tables and Cloud Storage files. It also provides several additional features:

• Using Streaming SQL extensions for defining time windows and calculating window-based statistics;
• Integration with Cloud Data Catalog for storing the schema of Cloud Pub/Sub topics and Cloud Storage file sets—a key enabler for using SQL with streaming messages;
• A simple-to-use graphical editor available in the BigQuery web UI. If you are familiar with BigQuery’s SQL editor, you can create Cloud Dataflow SQL jobs. 

To switch the BigQuery web UI to Cloud Dataflow SQL editing mode, open the BigQuery web UI, go to More>Query settings and select “Cloud Dataflow” as the query engine.

As an example, let’s say you have a Cloud Pub/Sub stream of sales transactions, and you want to build a real-time dashboard for the sales managers of your organization showing them the up-to-date stats of the sales in their regions. You can accomplish this in a few steps: write a SQL statement like the following one, launch a Cloud Dataflow SQL job, direct the output to a BigQuery table, then use one of the many supported dashboarding tools, including Google Sheets, Data Studio, and others, to visualize the results.

  SELECT
  sr.sales_region,
  TUMBLE_START("INTERVAL 5 SECOND") AS period_start,
  SUM(tr.payload.amount) as amount
FROM pubsub.topic.`dataflow-sql`.transactions AS tr
  INNER JOIN bigquery.table.`dataflow-sql`.dataflow_sql_ds.us_state_salesregions AS sr
  ON tr.payload.state = sr.state_code
GROUP BY
  sr.sales_region,
  TUMBLE(tr.event_timestamp, "INTERVAL 5 SECOND")

In this example, we are joining the “transactions” Cloud Pub/Sub topic in the “dataflow-sql” project with a metadata table in BigQuery called “us_state_salesregions.” This table contains a mapping between the state codes (present in the “transactions” Cloud Pub/Sub topic) and the sales regions “Region_1”, “Region_2”, .., “Region_N” that are relevant to the sales managers in our example organization. In addition to the join, we’ll do a streaming aggregation of our data, using one of the several windowing functions supported by Cloud Dataflow. In our case, we will use TUMBLE windows, which will divide our stream into fixed five-second time intervals, group all the data in those time windows by the sales_region field, and calculate the sum of sales in the sales region. We also want to preserve the start of each time window, via TUMBLE_START("INTERVAL 5 SECOND"), to plot sales amounts as a time series. 

To start a Cloud Dataflow job, click on “Create Cloud Dataflow job” in the BigQuery web UI.

When data starts flowing into the destination table, it will contain three fields: the sales_region, the timestamp of the period start, and the amount of sales. 

In the next step, we will create a BigQuery Connected Sheet that shows a column chart of sales in “Region_1” over time. Select the destination BigQuery table in the nav tree. In our case, it’s the dfsqltable_25 table.

Then, select Export>Explore with Sheets to chart your data. Do it from the tab where your BigQuery table is shown, and not from the tab where your original Cloud Dataflow SQL was. In Sheets, create the column chart using the data connection to BigQuery, and choose period_start for the X-axis, amount as your data series, and add a sales_region filter. This is all you have to do to build a chart in Sheets that visualizes streaming data.

Mixing and joining data in Cloud Pub/Sub and BigQuery helps solve many real-time dashboarding cases, but quite a few customers have also asked for support of their Cloud Storage files to join those files with events in Cloud Pub/Sub or with tables in BigQuery. This is now possible in Dataflow SQL, enabled by our integration with Data Catalog’s Cloud Storage file sets. 

In the following example, you’ll see an archive of transactions stored in CSV files in the “transactions_archive” Cloud Storage bucket.

Two gcloud commands can define a Cloud Storage file set and entry group in Data Catalog.

  #Create a GCS entrygroup
gcloud beta data-catalog entry-groups create transactions_archive_eg --location=us-central1
#Create the fileset
gcloud beta data-catalog entries create transactions_archive_fs --location=us-central1 --entry-group=transactions_archive_eg --gcs-file-patterns=gs://dataflow-sql/inputs/transactions_archive/*.csv  --description="Historical Archive of Transactions"

Notice how we defined the file pattern “gs://dataflow-sql/inputs/transactions_archive/*.csv” as part of the file set entry definition. This pattern is what will allow Cloud Dataflow to find the CSV files once we write the SQL statement that references this file set.

We can even specify the schema of this transactions_archive_fs file set using a GUI editor. For that, go to the BigQuery web UI (make sure it is running in Cloud Dataflow mode), select “Add Data” in the left navigation and choose “Cloud Dataflow sources.” Search for your newly added Cloud Storage file set and add it to your active datasets in the BigQuery UI.

This will allow you to edit the schema of your file set after you select it in the nav tree. The “Edit schema” button is right there on the “Schema” tab. The visual schema editor is new and works for both Cloud Storage file sets as well as for Cloud Pub/Sub topics.

Once you’ve registered the file set in Data Catalog and defined a schema for it, you can query it in Cloud Dataflow SQL. In the next example, we’ll join the transactions archive in Cloud Storage with the metadata mapping table in BigQuery.

  SELECT
  sr.sales_region,
  TUMBLE_START("INTERVAL 5 SECOND") AS period_start,
  SUM(tr.amount) as amount
FROM datacatalog.entry.`dataflow-sql`.`us-central1`.transactions_archive_eg.transactions_archive_fs AS tr
  INNER JOIN bigquery.table.`dataflow-sql`.dataflow_sql_ds.us_state_salesregions AS sr
  ON tr.state = sr.state_code
GROUP BY
  sr.sales_region,
  TUMBLE(CAST(tr.tr_time_str AS TIMESTAMP), "INTERVAL 5 SECOND")

Notice how similar this SQL statement is to the one that queries Cloud Pub/Sub. The TUMBLE window function even works on Cloud Storage files, although we will define the windows based on a field “tr_time_str” that is inside the files (the Cloud Pub/Sub SQL statement used the tr.event_timestamp attribute of the Cloud Pub/Sub stream). The only other difference is the reference to the Cloud Storage file set. We accomplish this by specifying datacatalog.entry.`dataflow-sql`.`us-central1`.transactions_archive_eg.transactions_archive_fs AS tr

Because both of the job inputs are bounded (batch) sources, Cloud Dataflow SQL will create a batch job (instead of a streaming job created for the first SQL statement using a Cloud Pub/Sub source) which will join your Cloud Storage files with the BigQuery table, and write the results back to BigQuery.

And now you have both a streaming pipeline feeding your real-time dashboard from a Cloud Pub/Sub topic, as well as a batch pipeline capable of onboarding historical data from CSV files. Check out the SQL Pipelines tutorial to start applying your SQL skills for developing streaming (and batch) Cloud Dataflow pipelines.

FlexRS for cost-effective batch processing of events
While real-time streaming processing is an exciting use case that’s growing rapidly, many streaming practitioners know that every stream processing system needs a sprinkle of batch processing (as you saw in the SQL example). When you bootstrap a streaming pipeline, you usually need to onboard historical data, and this data tends to reside in files stored in Cloud Storage. When streaming events need to be reprocessed due to changes in business logic (i.e., time windows get readjusted, or new fields are added), this reprocessing is also better done in batch mode.

The Apache Beam SDK and the Cloud Dataflow managed service are well-known in the industry for their unified API approach to batch and streaming analytics. The same Cloud Dataflow code can run in either mode with just minimal changes (usually replacing the data source from Cloud Pub/Sub to Cloud Storage). Remember our SQL example, where switching the SQL statement from a streaming source to a batch source was no trouble at all? And while it’s easy to go back and forth between streaming and batch processing in Cloud Dataflow, an important factor influencing the processing choice is cost. Customers who gravitate to batch processing have always looked to find the right balance between the speed of execution and costs. In many cases, that may mean you can be flexible with the amount of time it takes to process a dataset if the overall cost of processing is reduced in a significant fashion. 

Our new Cloud Dataflow FlexRS feature reduces batch processing costs by up to 40% using advanced resource scheduling techniques and a mix of different virtual machine (VM) types (including the preemptible VM instances) to decrease processing costs while providing the same job completion guarantees as regular Cloud Dataflow jobs. FlexRS uses the Cloud Dataflow Shuffle service, which allows it to handle the preemption of worker VMs better because the Cloud Dataflow service does not have to redistribute unprocessed data to the remaining workers.

Using FlexRS requires no code changes in your pipeline and can be accomplished by simply specifying the following pipeline parameter:

--flexRSGoal=COST_OPTIMIZED

Running Cloud Dataflow jobs with FlexRS requires autoscaling, a regional endpoint in the intended region, and specific machine types, so you should review the recommendations for other pipeline settings. While Cloud Dataflow SQL does not yet support FlexRS, it will in the future.

Simultaneously with launching FlexRS in general availability, we are also extending its availability to five additional regions, covering all regions now where we have regional endpoints and Cloud Dataflow Shuffle:

• us-central1
• us-east1
• us-west1
• europe-west1
• europe-west4
• asia-east1
• asia-northeast1

To learn more about Cloud Dataflow SQL, check out our tutorial and try creating your SQL pipeline using the BigQuery web UI. Visit our documentation site for additional FlexRS usage and pricing information.

Check out other recently launched streaming and batch processing features: 

• We deployed Cloud Dataflow Shuffle and Cloud Dataflow Streaming Engine to three additional regions, bringing total availability to seven regions.
• We launched the ability to protect your pipeline state with customer-managed encryption keys.

Python streaming support from Cloud Dataflow is now generally available.