At Google Cloud, we’re committed to making generative AI useful for everyone. Doing so requires more than making powerful foundation models available to businesses, governments, and developers. Models also need to be backed by platforms that make adoption faster and safer, with onramps to meet organizations wherever they are, regardless of their software or data science expertise. 

Today, we’re excited to announce general availability of Generative AI support on Vertex AI, giving customers access to our latest platform capabilities for building and powering custom generative AI applications. With this update, developers can access our text model powered by PaLM 2, Embeddings API for text, and other foundation models in Model Garden, as well as leverage user-friendly tools in Generative AI Studio for model tuning and deployment. Backed by enterprise-grade data governance, security, and safety features, Vertex AI can make it easier than ever for customers to access foundation models, customize them with their own data, and quickly build generative AI applications.

Vertex AI powers generative AI model customization for enterprise developers, data scientists, and everyone in between 

Foundation models are the starting point for creating customized generative AI applications—but models alone are not sufficient. That’s why in March, we announced Generative AI support on Vertex AI, the biggest-ever update to our machine learning platform, and began working with trusted testers. Now generally available to customers, Model Garden and Generative AI Studio leverage Google Cloud’s tight partnership with Google Research and Google DeepMind, making it easy for developers and data scientists to use, customize, and deploy models. 

Model Garden lets customers access and experiment with foundation models from Google and its partners, with over 60 models available and many more to come. In addition to making Model Garden, PaLM 2, and Embeddings API for text generally available, we’re also making our recently-announced Codey model for code completion, generation, and chat available for public preview. 

Along with these and other foundation models, Vertex AI offers a full ecosystem of tools to help builders tune, deploy, and govern models in production. For example, in May, we were the first enterprise ML platform to provide Reinforcement Learning with Human Feedback, or RLHF, which helps improve model usefulness and reduce cost. We’ve also upgraded Vertex AI’s suite of MLOps tools for model development and maintenance for customers who need to manage large models. With Generative AI Studio generally available, customers can now leverage an even wider range of tools, including multiple tuning methods for large models, that can significantly accelerate development of custom generative AI applications. 

We’re already seeing innovative results from early adopters via our trusted tester program and preview period. For example, leading global airline supplier GA Telesis is using our PaLM model on Vertex AI to build a data extraction solution that automatically synthesizes email orders and provides customers a quote. This eliminates the need for their sales teams to manually cross-reference emails with inventory availability. GitLab is leveraging our Codey model on Vertex AI for their “Explain this Vulnerability” feature, which gives their users a natural language description of code vulnerabilities, along with recommendations for resolving them. Canva, the visual communication platform, is using Google Cloud’s rich generative AI capabilities in language translation to better support its non-English speaking users, letting users easily translate presentations, posters, social media posts, and more into over a hundred languages. The company is also testing ways that Google’s PaLM technology can turn short video clips into longer, more compelling stories. 

And today, we’re pleased to share that Typeface, and DataStax are also building new generative AI capabilities with Vertex AI.    

Now is the time to build 

These announcements add to our news yesterday that we’ve added expanded access to Enterprise Search on Generative AI App Builder (Gen App Builder), allowing businesses to create custom chatbots and search engines that combine generative AI with Google’s semantic search technologies. Gen App Builder offers out-of-box solutions to common generative AI use cases, Vertex AI’s expansive platform capabilities can accelerate wide-ranging innovation, and growing ecosystem support from partners help our customers build freely. Together, these technologies and partnerships mean the full spectrum of developers and data scientists, from novices to seasoned experts, can build generative AI apps with enterprise-ready services on Google Cloud. 

As with our entire Cloud portfolio, Vertex AI and Gen App Builder help give customers complete control over their data; it doesn’t need to leave the customer’s tenant, is encrypted both in transit and at rest, and is not shared or used to train Google models. Google rigorously evaluates our new models to ensure they meet our Responsible AI Principles, and all of our generative AI offerings include the user security, data management, and access controls Google Cloud customers have come to expect. 

We’re grateful to our trusted testers for their integral role in bringing Generative AI support on Vertex AI to market, and we look forward to seeing what customers across all industries create with our growing catalog. To learn more about Google Cloud’s generative AI products, visit our solutions page, and to keep up with our latest AI news, don’t miss “The Prompt” or our generative AI primer for executives on Transform with Google Cloud.

Retail will forever be an industry that must constantly reinvent itself in response to, and anticipation of, ever-changing consumer demands.

Digital transformation is fueling these changes and we’ve previously spoken about how businesses including Ulta Beauty and Kohl’s are taking advantage of Google Cloud to put data at the center of what they do and deliver the best possible shopping experience and product offerings for their customers.

Leveraging Google Cloud machine learning platform, Sainsbury is able to develop predictive analytics models to spot trends and adjust inventory, providing shoppers with a better experience. 

Sainsbury’s, one of Britain’s best-known supermarkets, is another great example of a business transforming the way it engages with its customers with the cloud.

With over 150 years of service, Sainsbury’s vision is to be the most trusted retailer, where people love to work and shop. It makes customers’ lives easier, by offering great quality and service at fair prices. 

The food industry and the way that customers shop is rapidly changing. From foodie hashtags on Instagram, to the latest cooking fads, customers want to stay connected to the latest trends and Sainsbury’s is empowering them do that.

To help Sainsbury’s achieve this goal, its Commercial and Technology teams, in partnership with Accenture, are building cutting-edge machine learning solutions on Google Cloud Platform (GCP) to provide new insights on what customers want and the trends driving their eating habits.

With the help of Google Cloud Platform, we are generating new insights into how the world eats and lives, to help us stay ahead of market trends and provide an even better shopping experience for our customers.
–Phil Jordan, Group CIO, Sainsbury’s 

Sainsbury’s solution relies on data from multiple structured and unstructured sources. Using Google Cloud’s powerful cloud-based analytics tools to ingest, clean and classify that data, and a custom-built front-end interface for internal users to seamlessly navigate through a variety of filters and categories, Sainsbury’s is able to gain advanced insights in real time.

As a result, Sainsbury’s has been able to develop predictive analytics models to spot trends and adjust inventory, providing shoppers with a better experience. 

Phil Jordan, Group CIO of Sainsbury’s believes this project will have a big impact.

“The grocery market continues to change rapidly. We know our customers want high quality at great value and that finding innovative and distinctive products is increasingly important to them. With the help of Google Cloud Platform, we are generating new insights into how the world eats and lives, to help us stay ahead of market trends and provide an even better shopping experience for our customers.” 

This project is also a great example of the successes Google Cloud customers have when they work with the company’s partners.

“We’re delighted to partner with Google Cloud to help the Sainsbury’s Commercial team apply predictive analytics to the identification of new and emerging trends in grocery,” says Adrian Bertschinger, Managing Director for Retail, Accenture.

“The food sector is experiencing significant, rapid disruption, and this new, cloud-based insights platform will help Sainsbury’s identify trends much earlier and adapt their product assortment in a faster, more informed way—all for the benefit of customers.” 

Whatever the next food or shopping trend may be, Sainsbury’s is looking to the cloud to help them stay a step ahead. 

While you likely know that data science is the practice of making data useful, you may not have a clear landscape around the tools that can aid each stage of the data science workflow as you use machine learning to tackle your challenges.

Read on to discover the six broad areas that are critical to the process of making data useful, and some corresponding Google Cloud products and services for those areas.

https://youtube.com/watch?v=EQvLUMjz-g4%3Fenablejsapi%3D1%26

Data engineering 

Perhaps the greatest missed opportunities in data science stem from  data that exists somewhere, but hasn’t been made accessible for use in further analysis. Laying the critical foundation for downstream systems, data engineering involves the transporting, shaping, and enriching of data for the purposes of making it available and accessible.

Data ingestion and data preprocessing on Google Cloud

Here we consider data ingestion as moving data from one place to another, and data preparation the process of transformation, augmentation, or enrichment prior to consumption. Global scalability, high throughput, real-time access, and robustness are common challenges in this stage. For scalable, real-time, and batch data processing, look into building data ingestion and preprocessing pipelines with Dataflow, a managed Apache Beam service. There’s a reason why Dataflow is called the backbone of analytics on Google Cloud. 
If you’re looking for a scalable messaging system to help you ingest data, consider Cloud Pub/Sub, a global, horizontally scalable messaging infrastructure. Cloud Pub/Sub was built using the same infrastructure component that enabled Google products, including Ads, Search, and Gmail, to handle hundreds of millions of events per second. 
If you want an easy way to automate data movement to BigQuery, a serverless data warehouse on Google Cloud, look into the BigQuery Data Transfer Service. For transferring data to Cloud Storage, take a look at the Storage Transfer Service. Or, for a no-code data ingestion and transformation tool, check out Data Fusion, which has over 150 preconfigured connectors and transformations. In addition to Dataflow and Data Fusion for data preparation, Spark users may want to look at related products and features for Spark on Google Cloud.

Data storage and data cataloging on Google Cloud

For structured data, consider a data warehouse like BigQuery, or any of the Cloud Databases (relational ones like Cloud SQL and NoSQL ones like Cloud BigTable and Cloud Firestore). For unstructured data, you can always use Cloud Storage. You may also want to consider a data lake. For data discovery, cataloging, and metadata management, consider Data Catalog. For a unified solution, take a look at Dataplex, which integrates a unified data management solution with an integrated analytics experience.

Learn more about data engineering on Google Cloud

• Explore the data engineering learning path
• Discover reference patterns
• Get certified by Google Cloud as a Professional Data Engineer

Data Analysis 

From descriptive statistics to visualizations, data analysis is where the value of data starts to appear.

Data exploration, data preprocessing, and data insights

Data exploration, a highly iterative process, involves slicing and dicing data via data preprocessing before data insights can start to manifest through visualizations or simply via simple group-by, order-by operations. One hallmark of this phase is that the data scientist may not yet know which questions to ask about the data. In this somewhat ephemeral phase, a data analyst or scientist has likely uncovered some aha-moments, but hasn’t shared them yet. Once insights are shared, the flow enters the Insights Activation stage, where those insights become used to guide business decisions, influence consumer choices, or become embedded in other applications or services. 

On Google Cloud, there are many ways to explore, preprocess, and uncover insights in your data. If you are looking for a notebook-based end-to-end data science environment, check out Vertex AI Workbench, which enables you to access, analyze, and visualize your entire data estate: from structured data at the petabyte-scale in SQL with BigQuery, to processing data with Spark on Google Cloud and its serverless, auto-scaling, and GPU acceleration capabilities. As a unified data science environment, Vertex AI Workbench also makes it easy to do machine learning with TensorFlow, PyTorch, and Spark, with built-in MLOps capabilities.

Finally, if your focus is on analyzing structured data from data warehouses and insight activation for business intelligence, you may want to also consider using Looker, with its rich interactive analytics, visualizations, dashboarding tools, and Looker Blocks to help you accelerate your time-to-insight.

Learn more about data analysis on Google Cloud

• Learn about Vertex AI Workbench for a Jupyter-based fully managed notebook environment
• Learn about how you can use BigQuery for petabyte-scale data analysis
• Learn about Spark on Google Cloud
• Discover the data analyst learning path
• Explore reference patterns for common analytics use cases

Model development

From linear regression to XGBoost, from TensorFlow to PyTorch, the model development stage is where machine learning starts to provide new ways of unlocking value from your data. Experimentation is a strong theme here, with data scientists looking to accelerate iteration speed between models without worrying about infrastructure overhead or context-switching between tools for data analysis and tools for productionizing models with MLOps. 

To solve these challenges, once again, as a Jupyter-based fully managed, scalable, and enterprise-ready environment, Vertex AI Workbench makes it easy as the one-stop-shop for data science, combining analytics and machine learning, including Vertex AI services. Apache Spark, XGBoost, TensorFlow, and PyTorch are just some of the frameworks supported on Vertex AI Workbench. Vertex AI Workbench makes managing the underlying compute infrastructure needed for model training easy with the ability to scale vertically and horizontally, and with idle timeouts and auto shutdown capabilities to reduce unnecessary costs. Notebooks themselves can be used for distributed training and hyperparameter optimization, and they include Git integration for version control. Due to the significant reduction in context switching required, data scientists can build and train models 5x faster using Vertex AI Workbench than when using traditional notebooks. 

With Vertex AI, custom models can be trained and deployed using containers. You can take advantage of pre-built containers or custom containers to train and deploy your models.

For low-code model development, data analysts and data scientists can use SQL with BigQuery ML to train and deploy models (including XGBoost, deep neural networks, and PCA),  directly using BigQuery’s built-in serverless, autoscaling capabilities. Behind-the-scenes, BigQuery ML leverages Vertex AI to enable automated hyperparameter tuning, and explainable AI. For no-code model development, Vertex AI Training provides a point-and-click interface to train powerful models using AutoML, which comes in multiple flavors: AutoML Tables, AutoML Image, AutoML Text, AutoML Video, and AutoML Translation.

Learn more about model development on Google Cloud

• Learn about Vertex AI Workbench for a Jupyter-based fully managed notebook environment
• Learn more about Vertex AI

ML engineering 

Once a satisfactory model is developed, the next step is to incorporate all the activities of a well-engineered application lifecycle, including testing, deployment, and monitoring. And all of those activities should be as automated and robust as possible.

Managed datasets and Feature Store on Vertex AI provide shared repositories for datasets and engineered features, respectively, which provide a single source of truth for data and promote reuse and collaboration within and across teams. Vertex AI’s model serving capability enables deployment of models with multiple versions, automatic capacity scaling, and user-specified load balancing. Finally, Vertex AI Model Monitoring provides the ability to monitor prediction requests flowing into a deployed model and automatically alert model owners whenever the production traffic deviates beyond user-defined thresholds and previous historical prediction requests.

MLOps is the industry term for modern, well engineered ML services, with scalability, monitoring, reliability, automated CI/CD, and many other characteristics and functions that are now taken for granted in the application domain. The ML engineering features provided by Vertex AI are informed by Google’s extensive experience deploying and operating internal ML services. Our goal with Vertex AI is to provide everyone with easy access to essential MLOps services and best practices.

Learn more about ML engineering and MLOps on Google Cloud

• Follow the guides, tutorials and documentation for Vertex AI
• Watch this video to learn more about Vertex AI
• Discover the data scientist/machine learning engineer learning path
• Get certified as a Professional ML Engineer

Insights activation 

The insights activation stage is where your data has now become useful to other teams and processes. You can use Looker and Data Studio to enable use cases in which data is used to influence business decisions with charts, reports, and alerts.

Data can also influence customer decisions and as a result increase usage or decrease churn, for example. Finally, the data can also be used by other services to drive insights; these services can run outside Google Cloud, inside Google Cloud on Cloud Run or Cloud Functions, and/or using Apigee API Management as an interface.  

Learn more about insights activation on Google Cloud

• Watch this video to learn about building interactive ML apps using Looker and Vertex AI
• Learn about Looker, and Looker solutions for eCommerce, Digital Media and more
• Discover a gallery of interactive dashboards created with Data Studio 
• Watch this video to understand the difference between Cloud Run and Cloud Functions

Orchestration 

All of the capabilities discussed above provide the key building blocks to a modern data science solution, but a practical application of those capabilities requires orchestration to automatically manage the flow of data from one service to another. This is where a combination of data pipelines, ML pipelines, and MLOps comes into play.  Effective orchestration reduces the amount of time that it takes to reliably go from data ingestion to deploying your model in production, in a way that lets you monitor and understand your ML system.

For data pipeline orchestration, Cloud Composer and Cloud Scheduler are both used to kick off and maintain the pipeline. 

For ML pipeline orchestration, Vertex AI Pipelines is a managed machine learning service that enables you to increase the pace at which you experiment with and develop machine learning models and the pace at which you transition those models to production. Vertex Pipelines is serverless, which means that you don’t need to deal with managing an underlying GKE cluster or infrastructure. It scales up when you need it to, and you pay only for what you use. In short, it lets you just focus on building your data science pipelines. 

Learn more about orchestration on Google Cloud

• Read more about Cloud Composer for Airflow-based pipelines
• Try some example notebooks on Github with Vertex AI Pipelines
• Learn different ways to trigger Vertex AI Pipeline runs
• Read the whitepaper on Practitioners Guide to MLOps: A framework for continuous delivery and automation of machine learning

Summary

Google Cloud offers a complete suite of data management, analytics, and machine learning tools to generate insights from data. Want to learn more? Check out the following resources:

• Building the data science driven organization from the first principles

Special thanks to the following contributors to this blogpost: Alok Pattani, Brad Miro, Saeed Aghabozorgi, Diptiman Raichaudhuri, Reza Rokni.

Over the past few years, many organizations have experienced the benefits of migrating their SAP solutions to Google Cloud. But this migration can do more than reduce IT maintenance costs and make data more secure. By leveraging BigQuery, SAP customers can complement their SAP investments and gain fresh insights by consolidating enterprise data and easily extending it with powerful datasets and machine learning from Google. 

BigQuery is a leading cloud data warehouse, fully managed and serverless, and allows for massive scale, supporting petabyte-scale queries at super-fast speeds. It can easily combine SAP data with additional data sources, such as Google Analytics or Salesforce, and its built-in machine learning lets users operationalize machine learning models using standard SQL — all at a comparatively low cost. 

If your SAP-powered organization is looking to supercharge its analytics with the strength of BigQuery, read on for considerations and recommendations for modeling with SAP data. These guidelines are based on our real-world implementation experience with customers and can serve as a roadmap to the analytics capabilities your business needs.

Considerations for data replication 

Like most technology journeys, this one should start with a business objective. Keeping your intended business value and goals in mind is critical to making the right decisions in the early steps of the design process.

When it comes to replicating the data from an SAP system into BigQuery, there are multiple ways to do it successfully. Decide which method will work best for your organization by answering these questions:

• Does your business need real-time data? Will you need to time travel into past data?
• Which external datasets will you need to join with the replicated data?
• Are the source structures or business logic likely to change? Will you be migrating the SAP source systems any time soon? For instance, will you be moving from SAP ECC to SAP S/4HANA?

You’ll also need to determine whether replication should be done on a table-by-table basis or whether your team can source from pre-built logic. This decision, along with other considerations such as licensing, will influence which replication tool you should use.

Replicating on a table-by-table basis
Replicating tables, especially standard tables in their raw form, allows sources to be reused and ensures more stability of the source structure and functional output. For example, the SAP table for sales order headers (VBAK) is very unlikely to change its structure across different versions of SAP, and the logic that writes to it is also unlikely to change in a way that affects a replicated table. 

Something else to consider: Reconciliation between the source system and the landing table in BigQuery is linear when comparing raw tables, which helps avoid issues in consolidation exercises during critical business processes, such as period-end closing. Since replicated tables aren’t aggregated or subject to process-specific data transformation, the same replicated columns can be reused in different BigQuery views. You can, for instance, replicate the MARA table (the material master) once and use it in as many models as needed. 

Replicating pre-built logic
If you replicate pre-built models, such as those from SAP extractors or CDS views, you don’t need to build the logic in BigQuery, since you’re using existing logic. Some of these extraction objects have embedded delta mechanisms, which may complement a replication tool that can’t handle deltas. This will save initial development time, but it can also lead to challenges if you create new columns, or if customizations or upgrades change the logic behind the extraction. 

It’s also important to note that different extraction processes may transform and load the same source columns multiple times, which creates redundancy in BigQuery and can lead to higher maintenance needs and costs. However, replicating pre-built models may still be a good choice, since doing so can be especially useful for logic that tends to be immutable, such as flattening a hierarchy, or logic that is highly complex.

How you approach replication will also depend on your long-term plans and other key factors — for example, the availability (and curiosity) of your developers, and the time or effort they can put into applying their SQL knowledge to a new data warehouse. 

With either replication approach, bear in mind when designing your replication process that BigQuery is meant to be an append-always database — so post-processing of data and changes will be required in both cases. 

Processing data changes

The replication tool you choose will also determine how data changes are captured (known as CDC – change data capture). If the replication tool allows for it (for example as SAP SLT does) the same patterns described in the CDC with BigQuery documentation also apply to SAP data. 

Because some data, like transactions, are known to be less static than others (e.g., master data), you need to decide what should be scanned in real time, what will require immediate consistency, and what can be processed in batches to manage costs. This decision will be based on the reporting needs from the business.

Consider the SAP table BUT000, containing our example master data for business partners, where we have replicated changes from an SAP ERP system:

In an append-always replication in BigQuery, all updates are received as new records. For example, deleting a record in the source will be represented as a new record in BigQuery with a deletion flag. This applies to whether the records are coming from raw tables like BUT000 itself or pre-aggregated data, as from a BW extractor or a CDS view.

Let’s take a closer look at data coming particularly from the partners “LUCIA” and “RIZ”. The operation flag tells us whether the new record in BigQuery is an insert (I), update (U) or deletion (D), while the timestamps help us identify the latest version of our business partner.

If we want to find the latest updated record for the partners LUCIA and RIZ, this is what the query would look like:

  SELECT partner,
        ARRAY_AGG(i1 ORDER BY i1.recordstamp DESC LIMIT 1) AS row
FROM SAP_ECC.but000 i1 
WHERE partner in ('LUCIA','RIZ')
    GROUP BY partner

With the following result:

After identifying stale records for “LUCIA” and “RIZ” business partners, we can proceed to deleting all stale records for “LUCIA” if we do not want to retain the history. In this example, we are using a different table to which the same replication has been done, for the purpose of comparison and to check that all stale records have been deleted for the selection made and that we only kept last updated records. For example:

  DELETE SAP_HANA.but000 i1
WHERE
i1.recordstamp < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 HOUR) AND
i1.recordstamp < (SELECT MAX(recordstamp) FROM SAP_HANA.but000 i2
WHERE
i1.partner = i2.partner
and partner="LUCIA")

You can also use the following query to retrieve stale records for “LUCIA” partner before moving forward with deletion

  SELECT partner, operation_flag, recordstamp  FROM SAP_HANA.but000 i1
WHERE
i1.recordstamp < TIMESTAMP_SUB(CURRENT_TIMESTAMP(), INTERVAL 3 HOUR) 
AND
i1.recordstamp < (SELECT MAX(recordstamp) FROM SAP_HANA.but000 i2 
WHERE 
i1.partner = i2.partner
and partner="LUCIA")

Which produces all of the records, except the latest update:

Partitioning and clustering

To limit the number of records scanned in a query, save on cost and achieve the best performance possible, you’ll need to take two important steps: determine partitions and create clusters. 

Partitioning
A partitioned table is one that’s divided into segments, called partitions, which make it easier to manage and query your data. Dividing a large table into smaller partitions improves query performance and controls costs because it reduces the number of bytes read by a query.

You can partition BigQuery tables by:

• Time-unit column: Tables are partitioned based on a “timestamp,” “date,” or “datetime” column in the table.
• Ingestion time: Tables are partitioned based on the timestamp recorded when BigQuery ingested the data.
• Integer range: Tables are partitioned based on an integer column.

Partitions are enabled when the table is created, as in the example below.  A great tip is to always include the partition filter as shown on the left-hand side of the query.

Clustering
Clustering can be created on top of partitioned tables by applying the fields that are likely to be used for filtering. When you create a clustered table in BigQuery, the table data is automatically organized based on the contents of one or more of the columns in the table’s schema. The columns you specify are then used to colocate related data.

Clustering can improve the performance of certain query types — for example, queries that use filter clauses or that aggregate data. It makes a lot of sense to use them for large tables such as ACDOCA, the table for accounting documents in SAP S/4HANA. In this case, the timestamp could be used for partitioning, and common filtering fields such as the ledger, company code, and fiscal year could be used to define the clusters.

A great feature is that BigQuery will also periodically recluster the data automatically.

Materialized views

In BigQuery, materialized views are precomputed views that periodically cache the results of a query for better performance and efficiency. BigQuery uses precomputed results from materialized views and, whenever possible, reads only the delta changes from the base table to compute up-to-date results quickly. Materialized views can be queried directly or can be used by the BigQuery optimizer to process queries to the base table.

Queries that use materialized views are generally completed faster and consume fewer resources than queries that retrieve the same data only from the base table. If workload performance is an issue, materialized views can significantly improve the performance of workloads that have common and repeated queries. While materialized views currently only support single tables, they are very useful common and frequent aggregations like stock levels or order fulfillment.

Further tips on performance optimization while creating select statements can be found in the documentation for optimizing query computation.

Deployment pipeline and security

For most of the work you’ll do in BigQuery, you’ll normally have at least two delivery pipelines running — one for the actual objects in BigQuery and the other to keep the data staging, transforming, and updated as intended within the change-data-capture flows. Note that you can use most existing tools for your Continuous Integration / Continuous Deployment (CI/CD) pipeline — one of the benefits of using an open system like BigQuery. But, if your organization is new to CI/CD pipelines, this is a great opportunity to gradually gain experience. A good place to start is to read our guide for setting up a CI/CD pipeline for your data-processing workflow.  

When it comes to access and security, most end-users will only have access to the final version of the BigQuery views. While row and column-level security can be applied, as in the SAP source system, separation of concerns can be taken to the next level by splitting your data across different Google Cloud projects and BigQuery datasets. While it’s easy to replicate data and structures across your datasets, it’s a good idea to define the requirements and naming conventions early in the design process so you set it up properly from the start. 

Start driving faster and more insightful analytics

The best piece of advice we can give you is this: Try it yourself. Anyone with SQL knowledge can get started using the free BigQuery tier. New customers get $300 in free credits to spend on Google Cloud during the first 90 days. All customers get 10 GB storage and up to 1 TB queries/month, completely free of charge. In addition to discovering the massive processing capabilities, embedded machine learning, multiple integration tools, and cost benefits, you’ll soon discover how BigQuery can simplify your analytics tasks. 

If you need additional assistance, our Google Cloud Professional Services Organization (PSO) and Customer Engineers will be happy to help show you the best path forward for your organization. For anything else, contact us at cloud.google.com/contact.