In the “What type of data processing organisation” paper, we examined that you can build a data culture whether your organization consists mostly of data analysts, or data engineers, or data scientists. However, the path and technologies to become a data-driven innovator are different and success comes from implementing the right tech in a way that matches a company’s culture. In this blog we will expand the data engineering driven organizations and provide how it can be built from the first principles.

Not all organizations are alike. All companies have similar functions (sales, engineering, marketing), but not all functions have the same influence on the overall business decisions. Some companies are more engineering-driven, others are sales-driven, others are marketing-driven. In practice, all companies are a mixture of all these functions. In the same way, the data strategy can be more focused on data analysts, and others on data engineering. Culture is a combination of several factors, business requirements, organizational culture, and skills within the organization. 

Traditionally organizations that focused on engineering mainly came from technology driven digital backgrounds. They built their own frameworks or used programming frameworks to build repeatable data pipelines. Some of this is due to the way the data is received, the shape the data is received and the speed of the data arrival as well. If your data allows it, your organization can be more focused on data analysis, and not so much on data engineering. If you can apply an Extract-Load-Transform approach (ELT) rather than the classic Extract-Transform-Load (ETL), then you can focus on data analysis and might not need extensive data engineering capability. For example, data that can be loaded directly into the data warehouse allows data analysts to also do data engineering work and apply transformations to the data.

This does not happen so often though. Sometimes your data is messy, inconsistent, bulky, and encoded in legacy file formats or as part of legacy databases or systems, with a little potential to be actionable by data analysts. 

Or maybe you need to process data in streaming, applying complex event processing to obtain competitive insights in near real time. The value of data decays exponentially with time. Most companies can process data by the next day in batch mode. However, not so many are probably obtaining such knowledge the next second data is produced.

In these situations, you need the talent to unveil the insights hidden in that amalgam of data, either messy or fast changing (or both!). And almost as importantly, you need the right tools and systems to enable that talent too.

What are those right tools? Cloud provides the scalability and flexibility for data workloads that are required in such complex situations. Long gone are the times when data teams had to beg for the resources that were required to have an impact in the business. Data processing systems are no longer scarce, so your data strategy should not generate that scarcity artificially.

In this article, we explain how to leverage Google Cloud to enable data teams to do complex processing of data, in batch and streaming. By doing so, your data engineering and science teams can have an impact when (in seconds) after the input data is generated.

Data engineering driven organizations

When the complexity of your data transformation needs is high, data engineers have a central role in the data strategy of your company, leading to data engineering driven organization. In this type of organization, data architectures are organized in three layers: business data owners, data engineers, and data consumers.

Data engineers are at the crossroads between data owners and data consumers, with clear responsibilities:

• Transporting data, enriching data whilst building integrations between analytical systems and operational systems ( as in the real time use cases)
• Parsing and transforming messy data coming from business units into meaningful and clean data, with documented metadata
• Applying DataOps, that is, functional knowledge of the business plus software engineering methodologies applied to the data lifecycle
• Deployment of models and other artifacts analyzing or consuming data

Business data owners are cross-functional domain-oriented teams. These teams know the business in detail, and are the source of data that feeds the data architecture. Sometimes these business units may also have some data-specific roles, such as data analysts, data engineers, or data scientists, to work as interfaces with the rest of the layers. For instance, these teams may design a business data owner, that is the point of contact of a business unit in everything that is related to the data produced by the unit.

At the other end of the architecture, we find the data consumers. Again, also cross-functional, but more focused on extracting insights from the different data available in the architecture. Here we typically find data science teams, data analysts, business intelligence teams, etc. These groups sometimes combine data from different business units, and produce artifacts (machine learning models, interactive dashboards, reports, and so on). For deployment, they require the help of the data engineering team so that data is consistent and trusted.

At the center of these crossroads, we find the data engineering team. Data engineers are responsible for making sure that the data generated and needed by different business units gets ingested into the architecture. This job requires two disparate skills: functional knowledge and data engineering/software development skills. This is often coined under the term DataOps (which evolved from DevOps methodologies developed within the past decades but applied to data engineering practices).

Data engineers have another responsibility too. They must help in the deployment of artifacts produced by the data consumers. Typically, the data consumers do not have the deep technical skills and knowledge to take the sole responsibility for deployment of their artifacts.This is also true for highly sophisticated data science teams. So data engineers must add other skills under their belt: machine learning and business intelligence platform knowledge. Let’s clarify this point, we don’t expect data engineers to become machine learning engineers. Data engineers need to understand ML to ensure that the data delivered to the first layer of a model ( the input ) is correct. They will also become key when delivering that first layer of data in the inference path, as here the data engineering skills around scale / HA etc really need to shine.

By taking the responsibility of parsing and transforming messy data from various business units, or for ingesting in real time, data engineers allow the data consumers to focus on creating value. Data science and other types of data consumers are abstracted away from data encodings, large files, legacy systems, complex message queue configurations for streaming. The benefits of concentrating that knowledge in a highly skilled data engineering team are clear, notwithstanding that other teams (business units and consumers) may also have their data engineers to work as interfaces with other teams. More recently, we even see squads created with members of the business units (data product owners), data engineers, data scientists, and other roles. Effectively creating complete teams with autonomy and full responsibility over a data stream, from the incoming data down to the data driven decision with impact in the business.

Reference architecture – Serverless

The number of skills required for the data engineering team is vast and diverse. We should not make it harder by expecting the team to maintain the infrastructure where they run data pipelines. They should be focusing on how to cleanse, transform, enrich, and prepare the data rather than how much memory or how many cores their solution may require.

The reference architectures presented here are based on the following principles:

• Serverless no-ops technologies
• Streaming-enabled for low time-to-insight

We present different alternatives, based on different products available in Google Cloud:

• Dataflow, the built-in streaming analytics platform in Google Cloud
• Dataproc, the Google Cloud’s managed platform for Hadoop and Spark. 
• Data Fusion, a codeless environment for creating and running data pipelines

Let’s dig into these principles. 

By using serverless technology we eliminate the maintenance burden from the data engineering team, and we provide the necessary flexibility and scalability for executing complex and/or large jobs. For example, scalability is essential when planning for traffic spikes during mega Friday for retailers. Using serverless solutions allows retailers to look into how they are performing during the day. They no longer need to worry about resources needed to process massive data generated during the day.

The team needs to have full control and write their own code for the data pipelines because of the type of pipelines that the team develops. This is true either for batch or streaming pipelines. In batch, the parsing requirements can be complex and no off the shelf solution works. In streaming, if the team wants to fully leverage the capabilities of the platform, they should implement all the complex business logic that is required, without artificially simplifying the complexity in exchange for some better latency. They can develop a pipeline that achieves a low latency with highly complex business logic. This again requires the team to start writing code from first principles.

However, that the team needs to write code should not imply that they need to rewrite any existing piece of code. For many input/output systems, we can probably reuse code from patterns, snippets, and similar examples. Moreover, a logical pipeline developed by a data engineering team does not necessarily need to map to a physical pipeline. Some parts of the logic can be easily reused by using technologies like Dataflow templates, and use those templates in orchestration with other custom developed pipelines. This brings the best of both worlds (reuse and rewrite), while saving precious time that can be dedicated to higher impact code rather than common I/O tasks. The reference architecture presented has another important feature: the possibility to transform existing batch pipelines to streaming.

The ingestion layer consists of Pub/Sub for real time and Cloud Storage for batch and does not require any preallocated infrastructure. Both Pub/Sub and Cloud Storage can be used for a range of cases as it can automatically scale up with the input workload.

Once the data has been ingested, our proposed architecture follows the classical division in three stages: Extract, Transform, and Load (ETL). For some types of files, direct ingestion into BigQuery (following an ELT approach) is also possible.

In the transform layer, we primarily recommend Dataflow as the data process component. Dataflow uses Apache Beam as SDK. The main advantage of Apache Beam is the unified model for batch and streaming processing. As mentioned before, the same code can be adapted to run in batch or streaming by adapting input and output. For instance, switching the input from files in Cloud Storage to messages published in a topic in Pub/Sub.

One of the alternatives to Dataflow in this architecture is Dataproc, Google Cloud’s solution for managed Hadoop and Spark clusters. The main use case is for those teams that are migrating to Google Cloud but have large amounts of inherited code in Spark or Hadoop. Dataproc enables a direct path to the cloud, without having to review all those pipelines. 

Finally, we also present the alternative of Data Fusion, a codeless environment for creating data pipelines using a drag-and-drop interface. Data Fusion actually uses Dataproc as its Compute Engine, so everything we have mentioned earlier applies also to the case of Data Fusion. If your team prefers to create data pipelines without having to write any code, Data Fusion is the right tool.

So in summary, these are the three recommended components for the transform layer:

• Dataflow, powerful and versatile with a unified model for batch and streaming processing. Straightforward path to move from batch processing to streaming
• Dataproc, for those teams that want to reuse existing code from Hadoop or Spark environments.
• Data Fusion, if your team does not want to write any code.

Challenges and opportunities

Data platforms are complex. Having on top of that data responsibility the duty to maintain infrastructure is a wasteful use of valuable skills and talent. Often data teams end up managing infrastructure rather than focusing on analyzing the data. The architecture presented in this article liberates the data engineering team from having to allocate infrastructure and tweak clusters but instead to focus on providing value through data processing pipelines.

For data engineers to focus on what they do best, you need to fully leverage the cloud. A lift & shift approach from any on-premise installation is not going to provide that flexibility and liberation. You need to leverage serverless technologies. As an added advantage, serverless lets you also scale your data processing capabilities with your needs, and be able to respond to peaks of activity, however large these are.

Serverless technologies sometimes face the doubts of practitioners: will I be locked in with my provider if I fully leverage serverless? This is actually a question that you should be asking when deciding whether to set up your architecture on top of a provider. 

The components presented here for data processing are based on open source technologies, and fully interoperable with other open source equivalent components. Dataflow uses Apache Beam, which not only unifies batch and streaming, but also offers a widely compatible runner. You can take your code elsewhere to any other runner. For instance, Apache Flink or Apache Spark. Dataproc is a fully managed Hadoop and Spark based on the vanilla open source components of this ecosystem. Data Fusion is actually the Google Cloud version of CDAP, an open source project.

On the other hand, for the serving layer, BigQuery is based on standard Ansi SQL. Whereas in the case of Bigtable and Google Kubernetes Engine, Bigtable is compatible at API level with HBase, and Kubernetes is an open source component.

In summary, when your components are based on open source, like the ones included in this architecture, serverless does not lock you in. The skills required to encode business logic in the form of data processing pipelines are based on engineering principles that remain stable across time. The same principles apply if you are using Hadoop, Spark, or Dataflow or UI driven ETL tooling. In addition, there are now new capabilities, such as low-latency streaming, that were not available before. A team of data engineers that learn the fundamental principles of data engineering will be able to quickly leverage those additional capabilities.

Our recommended architecture separates the logical level, the code of your applications, from the infrastructure where they run. This enables data engineers to focus on what they do best and on where they provide the highest added value. Let your Dataflow and your engineers impact your business, by adopting the technologies that liberate them and allow them to focus on adding business value. To learn more about building an unified data analytics platform, take a look at our recently published Unified Data Analytics Platform paper and Converging Architectures paper.

As we think about economic recovery from COVID-19—both inside Google and outside through working with Google Cloud customers—we’ve made many important observations. Among them is the recognition that the ways software developers and IT practitioners work together will shift in the post COVID-19 world. Our economic recovery today will look different than past recoveries, and on a fundamental level, the way we innovate will be different than it’s ever been before.

Right now, we’re entering a new phase of cloud computing, where businesses have shifted from making tactical infrastructure decisions, to making larger IT decisions with an eye towards enabling transformation throughout the company. Data, and what we can do with that data, is key to this transformation. And how companies put data in the hands of every employee to help catalyze transformation and solve the most important and impactful opportunities in their industries is at the core.  

A recent Google-commissioned study by IDG highlighted the role of data analytics and intelligent solutions when it comes to helping businesses separate from their competition. The survey of 2,000 IT leaders across the globe reinforced the notion that the ability to derive insights from data will go a long way towards determining which companies win in this new era.

Data analytics and intelligence were prioritized during COVID-19

The results of the IDG study show a separation amongst those organizations that embrace the capabilities of today’s data analytics and AI/ML tools and those that do not. When COVID-19 hit, many organizations cancelled IT initiatives, with 55% of respondents delaying or cancelling at least one technology project. However, 32% of respondents accelerated or introduced initiatives around building out or improving the use of data analytics and intelligence. IT leaders realize how critical data is to their future success, even when resources are scarce.

Digital-focused companies are faster to embrace advanced intelligence tools

Furthermore, enthusiasm for big data analytics, AI, and ML technologies is highest among companies who are further along in their digital transformation journeys. Fifty-four percent of companies who identify as “Fully digitally transformed” or “Digital native” are using or considering using these tools, vs. the global average of 37%. And, these same organizations are embracing the promise of AI more than their peers. Forty-eight percent felt that “Embedded AI across our full stack of cloud solutions will be critical” vs 39% of digital conservatives. These companies realize these digital tools enable them to be more resilient, agile, and prepared for whatever the future brings.

Companies are turning to cloud to maximize insights from data

As companies tap into the promise of data analytics and AI/ML, they are turning to cloud for help. When considering which cloud providers to work with, 78% of respondents said big data analysis is a “must have” or a “major consideration,” which placed this capability at the top of the list of consideration factors. This is not surprising, as cloud solutions address the most common pain points and barriers to innovation. Three of the respondents’ top four areas impeding innovation are addressed by cloud: Insufficient IT & developer skill sets (1st), security risks and concerns (2nd), and legacy systems and technologies (4th). Plus, cloud makes it easier to quickly launch a project, scale up or scale down, and pay for only what you use.

COVID-19 changed the very nature of business, and of IT. It forced IT leaders to decide where to put their scarce resources and big data analytics and AI/ML were, understandably, at the top of the list. To learn more about the findings, download the IDG report “No turning back: How the pandemic reshaped digital business agendas.”

Skyscanner is a leading global travel search company covering flights, hotels, and car hire around the world. Founded in 2003, the company helps over 40 million people each month find the best travel options across its portfolio of websites and mobile apps.

Skyscanner wanted to understand the anonymized behaviour of consumers on a more granular level than was possible via standard reports in the Google Analytics Premium web interface or even using the reporting APIs.

“These methods work well for high-level analysis and standard marketing reporting, but we were keen to dig deeper into the data to get more insight and further optimise our products,” explains Mark Shilton, Principal Analyst in the Skyscanner Data Team.

For example, the company wanted to create detailed cohorts to understand how users interacted with Skyscanner over time. Also, different teams in the business were keen to understand the performance of individual pieces of functionality that fell within their remit.

To do this, they needed to understand not just the overall conversion rate, but how users who interact with a given piece of functionality convert compared to those who do not. Skyscanner also wanted drill down into specific markets, devices types, and marketing channels.

Mapping a plan for deeper insights

Skyscanner opted to address all of these needs by integrating Google Analytics Premium with BigQuery. The integration has become the starting point for many detailed investigations across the business.

For example, analysts and engineers now run cohort analyses to understand how frequently users return to Skyscanner and which channels are most effective at which part of the customer journey.

“This type of analysis is allowing a much deeper understanding of our marketing activity and is informing our future strategy and spend,” Mark says.

He reveals that using BigQuery in conjunction with other tools such as Tableau and Python also helps Skyscanner execute analysis much more quickly and efficiently than before.

“While in the past it was tricky to get a fully unsampled report based on specific segments of users flowing directly from Google Analytics Premium into a Tableau dashboard, now it is simply a matter of writing the query, creating a connection in Tableau to automatically refresh the data daily, and publishing this dashboard to the rest of the company.”

Another key benefit of using a flexible combination of tools is the ability to keep an eye on costs.

“Where the aggregations and segments of data are required on a regular basis, we have to consider the potential cost of querying the entire BigQuery dataset multiple times for the same data,” Mark explains. “To minimise this, we use Python scripts to automate these aggregations into new, smaller tables that are much more cost efficient to query.”

Excellent visibility and a clear path ahead

Combining Google Analytics Premium with BigQuery has supercharged Skyscanner’s ability to turn raw data into deep understanding of consumer behaviour.

“We have been using BigQuery for various pieces of analysis, from one-off investigations to powering daily dashboards,” Mark affirms. “In all cases it has speeded up our workflow and enabled us to gain greater insight more quickly. In a fast moving internet economy, this is key. Instead of setting up and scheduling one or more unsampled API reports, analysts can now write a query against BigQuery and have results almost instantly.”

“BigQuery has also allowed us to more easily isolate the effects of marketing from the effects of site changes,” Mark says. “By writing queries that focus on the conversion rate from specific pages in the funnel, we can better segment our traffic. We can separate traffic from various sources, including: specific marketing campaigns, users who make it to specific parts of the funnel, or users who interact with new functionality. This greater understanding has played a key role in improving overall conversion rates on our websites, particularly on mobile where we’ve achieved conversion rate improvements of 30 to 40% on smartphone and tablet devices in the last six months.”

Looking to the future, Skyscanner’s next steps include exploring how this data can be used to segment, cluster, and classify users for machine learning analyses, which would not have been possible using standard reporting functionality.

Patients about to undergo a healthcare procedure understandably want the best medical professionals they can get. But how can they know which doctors have had the most experience and the best outcomes with that particular procedure? How can they make an informed decision about which doctor to select when the information they have is limited to the doctor’s practice area and subjective reviews from other patients?

MD Insider is working to solve that problem using machine learning (ML) to objectively analyze doctor performance. By analyzing data from thousands of institutions and millions of doctor-patient interactions and medical events, MD Insider identifies physician performance insights based on their experience and outcomes. Insights are then integrated into a triage engine, that enables consumers to search for and schedule appointments with providers who meet their clinical criteria and convenience preferences, such as insurances accepted, office hours, locations, and language.

MD Insider also offers robust data APIs to help health systems, health plans, and employers reduce costs and improve quality of care. Payers use the APIs to curate high-quality provider networks and manage provider directories. Examples of MD Insider’s data APIs include Provider Experience and Share of Practice metrics, Provider Quality and Outcomes, Network Modeling, Expert Clinical Search Taxonomy, Find a Provider, Acute-Care Hospital Quality, and Provider and Facility Metadata.

“We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

MD Insider is continuously ingesting the latest performance data about physicians and analyzing billions of rows of data. Requiring constant scalability, the company was born in the cloud; however, it had difficulty configuring server instances for the optimal balance of memory and CPU, and its Hadoop cluster had to be kept running 24/7. Network performance was often slow for no apparent reason. As a result, failure rates from node timeouts increased, and costs grew along with the data. MD Insider had to estimate its usage and pay up front, and received little financial benefit from sustained use commitments.

Knowing that data would continue to grow, MD Insider decided to move its data services — the most demanding and complex portion of its infrastructure — to Google Cloud Platform (GCP), and took advantage of GCP managed services for container management and big data analytics.

“One of the reasons we decided to move to Google Cloud Platform is because it feels like a unified, well-designed cloud architecture and pricing model,” says Ed Holsinger, Lead Data Engineer and Head of Data Science at MD Insider. “We use Google Cloud Platform the way the cloud was supposed to be used. By comparison, other cloud providers feel like you’re renting somebody else’s data center.”

“Moving to Google Cloud Platform and using Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost. Our data scientists have more power than ever before to generate insights for our customers.”

—Ed Holsinger, Lead Data Engineer and Head of Data Science, MD Insider

5x the performance, 75% less cost

MD Insider now uses Kubernetes Engine to automate container management and deploy clusters in minutes with just a few clicks. When hundreds of machines are required to analyze a large dataset, automation in Kubernetes Engine deploys ML models as containers, each of which manages the full lifecycle of its task, including scaling up resources, deploying results, and scaling back down when the task is finished. It’s easy for MD Insider to specify exactly how much CPU and memory each container needs, helping maximize performance while reducing costs.

“Moving to Google Cloud Platform and Kubernetes Engine gave us 5x the processing power for analyzing physician performance at 75% less cost,” says Ed. “Our data scientists have more power than ever before to generate insights for our customers. Data scoring jobs that used to take three business days now take four hours.”

Adds Galen Meurer, Senior Software Engineer at MD Insider: “Even if all Google Cloud Platform had to offer was Kubernetes Engine, I would still want to use it. Previously we spent up to 30% of our time managing our container infrastructure, which we can now use for product development.”

A foundation for data science

MD Insider was happy to find that GCP offers a wide variety of managed services. For example, the company is supplementing its Kubernetes Engine clusters with BigQuery for its big data masters and selection jobs, enabling scientists to analyze new and different types of data as well as analyze larger datasets in less time. MD Insider also uses Cloud Storage for big data staging and Cloud Dataproc to run managed Apache Spark clusters for data processing.

“Google Cloud Platform gives us an incredibly powerful cloud architecture for data engineering and data science,” says Eric Wilson, CEO of MD Insider. “That gives our scientists independence, they can do what they need to do without waiting and with no contention between them.”

A developer-friendly platform

Migrating its data services was such a success that MD Insider decided to move the rest of its infrastructure to GCP, including the front end for its web application. Since the migration, MD Insider has experienced no unplanned downtime on GCP, allowing it to easily meet the 99.5% uptime SLA it promises to customers. It’s also taking advantage of Build Triggers in Kubernetes Engine to automate container builds and reduce build times by more than 40%. Production code can be updated in seconds, with no impact to end users other than making new features available.

“GCP has simplified our workflow in so many ways, from intelligent load balancing to content delivery and automating builds,” says Matthew Frey, Software Engineer. “Everything on GCP is cohesive and developer friendly, with a superior UI and better network performance than other cloud providers.”

Ryan Beaini, Senior Software Engineer at MD Insider, agrees: “Since we moved to GCP, our developers are definitely happier. The pain and the headaches we experienced because of the limitations of our previous toolset all went away.”

“We’re a small company, but what we’re doing is incredibly important. We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

—Eric Wilson, CEO, MD Insider

Securing billions of rows of clinical and non-clinical healthcare data

As a healthcare technology company, MD Insider processes billions of rows of clinical and non-clinical healthcare data. To control user access to GCP, it uses Identity & Access Management (IAM) along with Yubico YubiKeys for hardware-based two-factor authentication when logging into Google Workspace. MD Insider takes comfort that GCP encrypts data at rest by default, and encrypts and authenticates data in transit when data moves outside physical boundaries not controlled by Google or on behalf of Google.

“On GCP, everything that we need to be encrypted for compliance purposes is encrypted, which is fantastic,” says Eric. “When I tell our potential clients and partners about the resources that Google has dedicated to security, it gives them the confidence that their data will be protected.”

Transforming how teams work

As a growing company, MD Insider must collaborate seamlessly between offices in California, Colorado, and Illinois. It relies on Google Workspace for communication and productivity, using Docs, Sheets, and Slides to drive the business. Employee and team files are stored in Drive, and meetings are conducted via Google Meet with Chromebox for Meetings videoconferencing hardware kits. Google Workspace also helps MD Insider maintain information security by authenticating email domains with digital signatures in Gmail and scanning outgoing email using Gmail Data Loss Prevention (DLP).

“I use Google Workspace every day, and everyone else here does too,” says Eric. “Team Drives are a big time saver for us. We’ve let our previous office software expire, because there’s no need to pay for those licenses anymore.”

Promoting healthcare transparency

With GCP helping MD Insider increase velocity and momentum, the company is making exciting progress. For example, it has entered into a strategic partnership with Zelis Healthcare, which will use MD Insider’s API to provide insights for a next-gen analytics platform that will give health plans unprecedented transparency around physician performance.

“We’re a small company, but what we’re doing is incredibly important,” says Eric. “We’re helping people make decisions about healthcare providers that could impact their lives and even be life-saving. Google Cloud Platform is helping us make our mission bigger, better, and brighter.”

*Google Workspace was formerly known as G Suite prior to Oct. 6, 2020.