Every organization has its own unique data culture and capabilities. Yet each is expected to use technology trends and solutions in the same way as everyone else. Your organization may be built on years of legacy applications, you may have developed a considerable amount of expertise and knowledge, yet you may be asked to adopt a new approach based on a technology trend. On the other hand, you may be on the other side of the spectrum, a digitally native organization built with engineering principles from scratch without legacy systems but expected to follow the same principles as process driven, established organizations. The question is, should we treat these organizations in the same way when it comes to data processing? In this series of blogs and papers this is what we are exploring: how to set up an organization from the first principles from data analyst, data engineering and data science point of view. In reality, there is no such organization that is solely driven by one of these but it is likely to be a combination of multiple types. What type of organization you become is then driven by how much you are influenced by each of these principles. 

When you are considering what data processing technology encompasses, take a step back and make a strategic decision based on your key goals. This can be whether you optimize for performance, cost, reduction in operational overhead, increase in operational excellence, integration of new analytical and machine learning approaches. Or perhaps you’re looking to leverage existing employees’ skills while meeting all your data governance and regulatory requirements. We will be exploring these different themes and will focus on how they guide your decision-making process. You may be coming from technologies which are solving some of the past problems and some of the terminologies may be more familiar, however they don’t scale your capabilities. There is also the opportunity cost of prioritizing legacy and new issues that arise from a transformation effort, and as a result your new initiative can set you further behind on your core business while you play catch up to an ever changing technology landscape. 

Data value chain

The key for any ingestion and transformation tool is to extract data from a source and start acting on it. The ultimate goal is to reduce the complexity and increase the timeliness of the data. Without data, it is impossible to create a data driven organization and act on the insights. As a result, data needs to be transformed, enriched, joined with other data sources, and aggregated to make better decisions. In other words, insights on good timely data mean good decisions.

While deciding on the data ingestion pipeline, one of the best approaches is to look into the volume of data, the velocity of the data, and type of data that is arriving. Other considerations include the number of different data sources you are managing, whether you need to scale to thousands of sources using generic pipelines, whether you want to create one generic pipeline but then apply data quality rules and governance. ETL tools are ideal for this use case as generic pipelines can be written and then parameterized. 

On the other hand, consider the data source. Can the data be directly ingested without transforming and formatting the data? If the data does not need to be transformed and can be ingested directly into the data warehouse as a managed solution. This not only reduces the operational costs but also allows for more timely data delivery. If the data is coming in through an unstructured format such as XML or in a format such as EBCDIC and needs to be transformed and formatted, then a tool with ETL Capabilities can be used depending on the speed of the data arrival. 

It is also important to understand the speed and time of arrival of the data. Think about your SLAs and time durations/windows that are relevant for your data ingestion plans. This would not only drive the ingestion profiles but would also dictate which framework to use. As discussed above, velocity requirements would drive the decision-making process.

Type of Organization

Different organizations can be successful by employing different strategies based on the talent that they have. Just like in sports, each team plays with a different strategy with the ultimate goal of winning. 

Organizations often need to decide on what’s the best strategy to take in respect to data ingestion and processing – whether you need to hire an expensive group of data engineers, or exploit your data wizards and analysts to enrich and transform data that can be acted on, or whether it would be more realistic to train the current workforce to do more functional/high value work rather than to focus on building generally understood and available foundational pieces.

On the other hand, the transformation part of ETL pipelines as we know it, dictates where the load will be. All of these are made a reality in the cloud native world where data can be enriched, aggregated, and joined. Loading data into a powerful and modern data warehouse means that you can already join and enrich the data using ELT. Consequently, ETL isn’t really needed in its strict terms anymore if the data can be loaded directly into the data warehouse.

All of the above was not possible in the traditional, siloed, and static data warehouses and data ecosystems whereby systems would not talk to each other or there were capacity constraints in respect to both storing and processing the data in the expensive Data Warehouse. This is no longer the case in the BigQuery world as storage is now cheap and transformations are now much more capable without constraints of virtual appliances. 

If your organization is already heavily invested into an ETL tool, one option is to use them to load BigQuery and transform the data initially within the ETL tool. Once the as-is and to-be are verified to be matching, then with the improved knowledge and expertise one can start moving workloads into BigQuery SQL, and effectively do ELT. 

Furthermore, if your organization is coming from a more traditional data warehouse that extensively relies on stored procedures and scripting, then the question that one may ask is, do I continue leveraging these skills and expertise and use these capabilities that are also provided in BigQuery? ELT with BigQuery is more natural, similar to what’s already in Teradata BTEQ, Oracle PL/SQL but migrating from ETL to ELT requires changes. This change then enables exploiting streaming use cases, such as real-time use cases in retail. This is because there is no preceding step before data is loaded and made available.

Organizations can be broadly classified under 3 types as Data Analyst Driven, Data Engineering driven, and Blended organization. We will be covering a Data Science driven organization within the Blended category.   

Data Analyst Driven

Analysts understand the business and are used to using SQL/spreadsheets. Allowing them to do advanced analytics through interfaces that they are accustomed to enables scaling. As a result, easy to use ETL tooling to bring data quickly into the target system becomes a key driver. Ingesting data directly from a source or staging area then also becomes critical as it allows analysts to exploit their key skills using ELT and increases timeliness of the data. This is commonplace with traditional EDWs and realized by extended capabilities of using Stored Procedures and Scripting. Data is enriched, transformed, and cleansed using SQL and ETL tools act as the orchestration tools. 

The capabilities brought by cloud computing on separation of data and computation changes the face of the EDW as well. Rather than creating complex ingestion pipelines, the role of the ingestion becomes, bringing data close to the cloud, staging on a storage bucket or on a messaging system before being ingested into the cloud EDW. This then releases data analysts to focus on looking into data insights using tools and interfaces that they are accustomed to. 

Data Engineering / Data Science Driven 

Building complex data engineering pipelines is expensive but enables increased capabilities. This allows creating repeatable processes and scaling the number of sources. Once complemented with cloud it enables agile data processing methodologies. On the other hand, data science organizations allow carrying out experiments and producing applications that work for specific use cases but are not often productionised or generalized. 

Real-time analytics enables immediate responses and there are specific use cases where low latency anomaly detection applications are required to run. In other words, business requirements would be such that it has to be acted upon as the data arrives on the fly. Processing this type of data or application requires transformation done outside of the target.

All the above usually requires custom applications or state-of-the-art tooling which is achieved by organizations that excel with their engineering capabilities. In reality, there are very few organizations that can be truly engineering organizations. Many fall into what we call here as the blended organization.  

Blended org

The above classification can be used on tool selection for each project. For example, rather than choosing a single tool, choose the right tool for the right workload, because this would reduce operational cost, license cost and use the best of the tools available. Let the deciding factor be driven by business requirements: each business unit or team would know the applications they need to connect with to get valuable business insights. This coupled with the data maturity of the organization would be the key to making sure the right data processing tool would be the right fit. 

In reality, you are likely to be somewhere on a spectrum. Digital native organizations are likely to be closer to being engineering driven, due to their culture and business that they are in. However, brick and mortar organizations would be closer to being analyst driven due to the significant number of legacy systems and processes they possess. These organizations are either considering or working toward digital transformation with an aspiration of having a data engineering / software engineering culture like Google. 

The blended organization with strong skills around data engineering, would have built the platform and built frameworks, to increase reusable patterns would increase productivity and then reduce costs. Data engineers focus on running Spark on Kubernetes whereas infrastructure engineers focus on container work. This in turn provides unparalleled capabilities as application developers focus on the data pipelines and even the underlying technologies or platforms changes code stays the same. As a result, security issues, latency requirements, cost demands and portability are addressed at multiple layers. 

Conclusion – What type of organization are you?

Often an organization’s infrastructure is not flexible enough to react to a fast changing technological landscape. Whether you are part of an organization which is engineering driven or analyst driven, organizations frequently look at technical requirements that inform which architecture to implement. But a key, and frequently overlooked, component needed to truly become a data-driven organization is the impact of the architecture on your data users. When you take into account the responsibilities, skill sets, and trust of your data users, you can create the right data platform to meet the needs of your IT department as well as your business.

To become a truly data-driven organization, the first step is to design and implement an analytics data platform that meets your technical and business needs. The reality is that each organization is different and has a different culture, different skills, and capabilities. Key is to leverage its strengths to stay competitive while adopting new technologies when it is needed and as it fits to your organization. 

To learn more about the elements of how to build an analytics data platform depending on the organization you are, read our paper here.

As data environments become more complex, companies are turning to streaming analytics solutions that analyze data as it’s ingested and deliver immediate, high-value insights into what is happening now. These insights enable decision makers to act in real time to take advantage of opportunities or respond to issues as they occur.

While understanding what is happening now has great business value, forward-thinking companies are taking things a step further, using real-time analytics integrated with artificial intelligence (AI) and business intelligence (BI) to answer the question, “what might happen in the future?” Arkansas-based Tyson Foods has embraced AI/BI analytics to enable predictive insights that unlock new opportunities and drive future growth.

Creating a digital twin for connected intelligence company wide

Before using AI/BI, Tyson’s analytics capabilities consisted of traditional BI solutions focused on KPIs and simplifying data so that humans could understand it. Tyson wanted to leverage its data to uncover ways to improve current processes and grow its business. But with BI alone, Tyson struggled to use data to run the simulations and scenarios essential to make educated decisions. To keep growing, it had to embrace the complexity of its data, building ways to analyze it and use it to inform decision making. 

Tyson’s on-premises analytics solutions limited its ability to be aggressive and make intelligent, timely, prescriptive decisions. The solution was to create a digital twin to scale optimizations within business processes, moving from local optimizations to system-wide connected optimizations. Doing so meant shifting entirely to cloud computing, with an initial focus on building the ingestion component of the digital twin platform.

Investing in a digital twin enabled Tyson to accelerate new capabilities like supply chain simulation “what-if” scenarios, prescriptive price elasticity recommendations, and improvement of customer intimacy. 

Solving the ingestion problem for faster time to insights

Before its migration to Google Cloud, analytics projects that Tyson suffered from uncertainty over how to obtain the data. This problem was prolific and caused project times to be extended for weeks or even months due to the need to write and support one-off data ingestion processes at the front end. This problem also prevented the IT team from delivering analytics solutions fast enough for the business to take full advantage of them. 

To solve this analytics problem, the team created Data Ingestion Compute Engine (DICE). DICE is a Google Cloud-hosted, open-source, cloud-native ingestion platform developed to provide configuration-based, no-ops, code-free ingestion from disparate enterprise data systems, both internal and external. It is centered on three high-level goals:

1. Accelerate the speed of delivery of IT analytics solutions
2. Enable growth of IT capabilities to produce meaningful insight
3. Reduce long-term total cost of ownership for ingestion solutions

Creating DICE ingestion platform with Google Cloud services

Teams use DICE to set up secure data ingestion jobs in minutes without having to manage complex connections or write, deploy, and support their own code. DICE enables unbound scale, highly parallel processing, DevSecOps, open source, and the implementation of Lambda Data Architecture.

A DICE job is the logical unit of work in the DICE platform, consisting of immutable and mutable configurations persisted as JSON documents stored in Firestore. The job exists as an instruction set for the DICE data engine, which is Apache Beam running Dataflow to instruct which data to pull, how to pull it, how often to pull it, how to process it, when it changes, and where to direct it.

Two of DICE’s primary layers include the metadata engine and the data engine. The metadata engine is responsible for the creation and management of DICE job configuration and orchestration. It is made up of many microservices that interact with multiple Google Cloud services, including the job configuration creation API, job build configuration helper API, and job execution scheduler API.

The data engine is responsible for the physical ingestion of data, the change detection processing of that data, and the delivery of that data to specified targets. The data engine is Java code that uses the Apache Beam unified programming model and runs in Dataflow. It is comprised of streaming, jobs, and Dataflow flex template batch jobs. Logically, the data engine is segmented across three layers: the inbound processing layer, the DICE file system layer, and the target processing layer, which takes the data from the DICE file system and moves it to targets.

Rolling DICE for thousands of ingestion jobs each day

DICE was first deployed to a production environment in November 2019, and just two years later, it has more than 3,000 data ingestion jobs from more than a hundred disparate data systems, both internal and external to Tyson Foods. Most of these jobs run multiple times a day. On a daily basis the DICE environment sees more than 25,000 Dataflow jobs running and an average of 3.25 terabytes of new data being ingested.

DICE supports ingestion from many different types of technologies, including BigQuery, SQL Server, SAP HANA, Postgres, Oracle, MySQL, Db2, various types of file systems, and FTP servers. Additionally, DICE supports target platform technologies for ingestion jobs that include multiple JDBC targets, multiple file system targets, and BigQuery and queue-based store and forward technologies. 

The platform continues to see linear growth of DICE jobs, all while keeping platform costs relatively flat. With increasing demand for the platform, Tyson’s IT team is constantly enhancing DICE to support new sources and targets.

This intelligent platform keeps adding new value and makes it simple for Tyson to take advantage of its data. This innovation is a necessity in this fast-changing world of digital business in which companies must transform a high volume of complex data into actionable insight.

Your product teams might ask – “Why does it take so long to build a feature or application?” Building applications is a heavy lift due to the technical complexity, which includes the complexity of backend services that are used to manage and store data. Every moment focused on this technical complexity is a distraction from  delivering on core business value. Firestore alters this by having Google Cloud manage your backend complexity through a complete backend-as-a-service! 

Firestore is a serverless, NoSQL document database that unlocks application innovation with simplicity, speed and confidence. 

It acts as a glue that intelligently brings together the complete Google Cloud backend ecosystem, in-app services from Firebase and core UI frameworks & OS from Google.

What is Firestore?

Firestore is a serverless, fully managed NoSQL document database that scales from zero to global scale without configuration or downtime. Here’s what makes Firestore unique:

• Ideal for rapid, flexible and scalable web and mobile development with direct connectivity to the database.
• Supports effortless real time data synchronization with changes in your database as they happen. 
• Robust support for offline mode, so your users can keep interacting with your app even when the internet isn’t available or is unreliable.
• Fully customizable security and data validation rules to ensure the data is always protected
• Built-in strong consistency, elastic scaling, high performance & best in class 99.999% availability 
• Integration with Firebase and Google Cloud services like Cloud Functions and BigQuery, serverless data warehouse.
• In addition to a rich set of Google Cloud service integrations, Firestore also offers deep one-click integrations with a growing set of 3rd party partners via Firebase Extensions to help you even more rapidly build applications.

Document-model database

Firestore is a Document-model database. All of your data is stored in “documents” and then “collections”.  You can think of a document as a JSON object. It’s a dictionary with a set of key-value mappings, where the values can be several different supported data types including strings, numbers or binary values..

These documents are stored in collections. Documents can’t directly contain other documents, but they can point to subcollections that contain other documents, which can point to subcollections, and so on. This structure brings with it a number of advantages. For starters, all queries that you make are shallow, meaning that you can grab a document without worrying about grabbing all the data underneath it. And this means that you can structure your data hierarchically in a way that makes sense to you logically, without having to worry about grabbing tons of unnecessary data. 

How to use Firestore?

Firestore can be used in two modes:

• Firestore in Native Mode:  This mode is differentiated by its ability to directly connect your web & mobile app to Firestore. Native Mode supports up to 10K writes per second, and over a million connections. 
• Firestore in Datastore Mode: This mode supports only server-side usage of Firestore, but supports unlimited scaling, including writes. 

Conclusion

Whatever your application use case may be, if you want to build a feature or an application quickly using Firestore backend-as-a-service. For a more in-depth look into Firestore check out the documentation. https://www.youtube.com/embed/moglAjmwmUQ?enablejsapi=1&

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

BigQuery offers strong query performance, but it is also a complex distributed system with many internal and external factors that can affect query speed. When your queries are running slower than expected or are slower than prior runs, understanding what happened can be a challenge.

The query execution graph provides an intuitive interface for inspecting query execution details. By using it, you can review the query plan information in graphical format for any query, whether running or completed.

You can also use the query execution graph to get performance insights for queries. Performance insights provide best-effort suggestions to help you improve query performance. Since query performance is multi-faceted, performance insights might only provide a partial picture of the overall query performance.

Execution graph

When BigQuery executes a query job, it converts the declarative SQL statement into a graph of execution, broken up into a series of query stages, which themselves are composed of more granular sets of execution steps. The query execution graph provides a visual representation of the execution stages and shows the corresponding metrics. Not all stages are made equal. Some are more expensive and time consuming than others. The execution graph provides toggles for highlighting critical stages, which makes it easier to spot the potential performance bottlenecks in the query.

Query performance insights

In addition to the detailed execution graph BigQuery also provides specific insights on possible factors that might be slowing query performance.

Slot contention

When you run a query, BigQuery attempts to break up the work needed by your query into tasks. A task is a single slice of data that is input into and output from a stage. A single slot picks up a task and executes that slice of data for the stage. Ideally, BigQuery slots execute tasks in parallel to achieve high performance. Slot contention occurs when your query has many tasks ready for slots to start executing, but BigQuery can’t get enough available slots to execute them.

Insufficient shuffle quota

Before running your query, BigQuery breaks up your query’s logic into stages. BigQuery slots execute the tasks for each stage. When a slot completes the execution of a stage’s tasks, it stores the intermediate results in shuffle. Subsequent stages in your query read data from shuffle to continue your query’s execution. Insufficient shuffle quota occurs when you have more data that needs to get written to shuffle than you have shuffle capacity.

Data input scale change

Getting this performance insight indicates that your query is reading at least 50% more data for a given input table than the last time you ran the query and hence experiencing query slowness. You can use table change history to see if the size of any of the tables used in the query has recently increased.

What’s next?

We continue to work on improving the visualization of the graph. We are working on adding additional metrics to each step and adding more performance insights that will make query diagnosis significantly easier. We are just getting started.

The ecommerce playing field has been hard to navigate for most retailers, and Cart.com is on a mission to change that. Traditionally, retailers needing to run their online store, order fulfillment, customer service, marketing, and other essential activities have had to cobble together systems to get the capabilities they need – much less having access to analytics across these functions. The result is costly, siloed ecommerce operations that are difficult to manage and scale.

It’s clearly not a formula for success, yet that’s the reality facing most retailers. Cart.com, in contrast, has set out to democratize ecommerce by giving brands of all sizes the full capabilities they need to take on the world’s largest online retailers. Our end-to-end environment empowers retailers to keep more of their revenue, set up proven strategies for managing all aspects of their business, and act on valuable insights from customer data every step of the way.

Together with our talented team, we’re building a unified ecommerce platform that already provides value to many leading or up and coming brands including Whataburger, GUESS, Dr. Scholl’s, Rowing Blazers, and Howler Bros. 

We’re excited about the opportunity ahead as we reimagine traditional approaches to online sales, fulfillment, marketing, accessing growth capital, providing a unified view of all ecommerce and marketing analytics, and other activities. Expectations for Cart.com are high, and we are building a company that can scale to $100B in revenue and beyond. Supported by the Startup Program by Google Cloud and Google Cloud solutions, we’re establishing a technology platform to transform all aspects of ecommerce for brands worldwide. 

Partner in disruption

At Cart.com, we’re currently targeting an underserved market. Our ideal customer is beyond demonstrating product-market-fit and is now at an inflection point seeking a growth opportunity. Typically, those companies are generating between $1M and $100M in annual revenue. We’ve seen an enthusiastic response from brands and retailers as well as investors, with backing from investors in just over a year totaling $143 million in three funding rounds.

Our strategy is to build an integrated ecommerce model that combines best-of-breed solutions, many of which we gain through acquisitions and then build upon to provide a streamlined and fully integrated experience for our brands. We’ve made seven acquisitions so far to round out our online store, order fulfillment, marketing services, customer service, and we have launched some integral partnerships including easy access to growth capital through our relationship with Clearco and product protection for customers on every purchase with Extend. Instead of acquiring a data company, we’re building our data platform on Google Cloud, across each operating function for a single-view for brands to harness actionable data. We see Google Cloud as the leader for data management, analytics, machine learning (ML) and artificial intelligence (AI).

Other reasons why we’re building our business on Google Cloud include scalability, excellence, security, reach, and data analytics that are far superior to other environments.

We also feel a cultural and mission alignment with Google Cloud and envision leaning into a long-term partnership of marketing, selling, and disrupting the disruptors together. Equally important to us are the investments Google Cloud is willing to make in early-stage companies like ours. The support through the Google Cloud for Startups program has been outstanding.

Built on Google Cloud

A wide range of Google Cloud solutions provide the foundation for our platform. For instance, Cloud Pub/Sub keeps our services communicating with one another. We rely on fully managed relational databases, like Cloud SQL and Cloud Spanner, to securely handle the huge volume of brand and shopper data generated every day.

Cloud Run allowed us to develop inside of containers before our Kubernetes infrastructure was ready to go. Now, we are taking advantage of all the capabilities in Google Kubernetes Engine. BigQuery integrates with all Google Cloud solutions and offers true data streaming natively out of the box, along with Dataflow for advanced analytics. We also use Container Registry to store and manage our Docker container images. Right now, we’re testing Cloud Composer to evaluate using it for data workflow orchestration instead of Apache Airflow.

The openness of the Google Cloud environment is further enabled by Anthos, which we may deploy soon to perform data integrations quickly as we acquire more companies over the next year. For example, if we acquire a company using Azure, we can easily align it with our Google Cloud ecosystem.

Enabling ecommerce 2.0

Recently, our team has been experimenting with Google Cloud Vertex AI and the fully managed services of AI deployment and ML operations. The capabilities would save us substantial time in the management of the ML lifecycle which allows us to focus more on developing proprietary AI that will transform commerce at scale.

Because Google Cloud is so far ahead in data science, our teams benefit from deep Google Cloud expertise as we look to provide brands with unmatched insights into customers to improve services and revenue. We’re also planning to test Recommendations AI among other tools to deploy customer product recommendations and personalization as turnkey productized offerings. Moving forward, we will likely use Bigtable to aid in serving machine learning to hundreds of thousands of brands due to its low latency and scalability.

Fanatical about brand success

We know that our work with Google Cloud for Startups and use of Google Cloud solutions for best-in-class data management, analytics, ML, and AI will enable us to offer even more transformative services to brands.

We also see the opportunity to use our platform and customer insights to break down barriers between brands, enabling retailers to share information and work better together when it’s in their best interests. What we’re building today on Google Cloud is fundamentally changing what’s possible for retailers of any size everywhere. 

As a startup, when recruiting talent or working with prospective customers, it helps to share our success with Google Cloud. We view them as an extension of the Cart.com team. It also validates our business as we continue building a more integrated, holistic approach to commerce that opens new opportunities and drives growth for brands worldwide.

For more details about Cart.com’s vision for unified ecommerce, check out our video.

If you want to learn more about how Google Cloud can help your startup, visit our page here to get more information about our program, and sign up for our communications to get a look at our community activities, digital events, special offers, and more.