Multi-Cloud Insights on Google BigQuery adastracorp.com Table of Contents Google BigQuery v/s Traditional Warehouses 3-4 BigQuery Architecture on Google Cloud 4-6 Data Experiences with BigQuery & Looker 6-7 Migrating to BigQuery with Adastra 7-8 Author 9 Organizations today have more data than ever before, and it is stored in multiple locations, in different APIs, SaaS, databases, and data centers. As a result, despite the unprecedented availability of data, deriving meaningful value from it has become more complex. While the Cloud has undoubtedly opened new avenues for extracting business insights from data, extracting multi-cloud insights remains challenging for many organizations. This roadblock is likely to be further exacerbated as an increasing number of businesses adopt a multi-cloud strategy to circumvent vendor lock-in issues and make the most of the tools offered by different cloud providers. As one of the Big 3 cloud platforms, Google’s data warehouse, BigQuery, and business intelligence tool, Looker, integrate seamlessly to enable users to break down data silos and extract actionable insights from data, regardless of where it is stored. This article showcases how Google BigQuery delivers multi-cloud insights and works with Looker to provide an enhanced data experience. It also highlights how Adastra can help organizations accelerate their migration journey while mitigating risks. Google BigQuery v/s Traditional Data Warehouses BigQuery is Google’s serverless, multi-cloud data warehouse that has been designed for business agility. As a Cloud-native data warehouse, it is fundamentally different from traditional data warehouse options available on public clouds today, which are essentially just legacy on-premise data warehouses that have been ported to the Cloud. As such, BigQuery overcomes many of the challenges associated with traditional data warehouses. For one, its instant, automatic scale up or down capabilities eliminate the need for manual, work-intensive scaling up. Unlike traditional data warehouses where scale down is often impossible, and sizing needs to be based on peak capacity, BigQuery can be right-sized as required to keep costs in control while meeting user SLAs. Since scaling up traditional warehouses is so expensive and resource-intensive, most organizations tend to keep environments locked and restricted to a few users. BigQuery offers unlimited scalability, so organizations can enable everyone across the organization to access the same datasets without worrying about performance bottlenecks. Another aspect of traditional data warehouses is that they have a heavy administrative or operational burden. BigQuery, on the other hand, is serverless, and the usual tasks of tuning configuration parameters, setting up availability, or planning and executing upgrades are automatically managed by Google. This frees up precious resources and allows users to focus on extracting insights, rather than worrying about the care and feeding of the data warehouse infrastructure. Traditional data warehouses are usually limited to a narrow set of workloads and are optimized for batch ingestion and BI and reporting use cases. In contrast, BigQuery has true record-by-record real-time streaming ingest for advanced AI use cases, IoT, etc. For example, with BigQuery ML, users can test, train, and run machine learning models natively without BigQuery using SQL. BigQuery Architecture on Google Cloud The central design principle of the BigQuery architecture is the decoupling of storage and compute for maximum flexibility. This allows users to scale storage and compute independently and pay only for what they use. BigQuery stores data in files in a columnar format, a highly performant format for analytics workloads, and those files are processed in parallel by clusters of compute resources. High availability is built-in, and storage is automatically replicated in multiple places across zones in the cloud region and even across cloud regions for seamless disaster recovery. Consequently, zero planned downtime is needed for maintenance, and in case a particular compute node fails, another one automatically takes its place. BigQuery’s unique architecture offers limitless scalability, so organizations can securely share a single view of their datasets with all users and provide query capacity for each team. With traditional data warehouses, however, scalability limitations often lead organizations to restrict access to a limited set of users, with each unit getting its own dataset and environment, and these siloes are a hindrance when it comes to extracting comprehensive or global insights. Also, users who cannot access data warehouse environments end up creating copies of the data, leading to data proliferation with different users working on different, conflicting versions of the truth. With BigQuery, organizations can allow all departments to access the same dataset with proper access permissions, so they can use their own compute resources with- out worrying about performance issues or data mismatch. BigQuery Omni is a relatively new solution that allows for multi-cloud analytics. While BigQuery runs natively on Google Cloud, BigQuery Omni brings the power of BigQuery to third-party cloud platforms. This means that users no longer need to move data from other clouds to Google Cloud to derive insights from their data, saving on time and data transfer costs. The core BigQuery Control plane still sits in Google Cloud, but it can be deployed in AWS (available in Preview) and Azure (coming soon) to process queries against data stored in AWS S3 or Azure Blob Storage. BigQuery is a vital component of the Google Cloud analytics platform. It integrates with Google’s data stores (including Cloud Storage, Cloud SQL, BigTable) and can run federated queries against data in these repositories. It also works seamlessly with other Google tools like Looker for business intelligence and insights. Google’s security and governance capabilities extend to BigQuery, allowing you to easily discover, understand, secure, and govern your data. Powering Data Experiences with BigQuery and Looker Business intelligence has evolved in recent years, and an increasing number of organizations expect democratized access to data for insights and reporting. However, many of the existing tools relied on inflexible pre-aggregated cubes, which led to data chaos when data came from different places. Looker takes BI to the next level with its Data-as-a-Platform model. It allows your data to stay where it lives (i.e., your database) and provides a semantic or governance layer for enforcement of business rules to enable citizen use of data. Looker integrates seamlessly with BigQuery and utilizes its ability to handle all types of data, essentially empowering users with the flexibility to query any data, regardless of whether it is nested, flattened, or gigantic. Business users have proper access controls and can leverage a single source of truth from the Cloud to get results and create different types of data experiences with Looker. Organizations have data in multiple locations, ranging from Google Ads to HubSpot, and they ideally want to consolidate all that data in one place, whether that is BigQuery, Postgres, Snowflake, or AWS. Looker then accesses that in-database architecture to get real-time data, allows users to send in SQL-based queries, and get results back from the database. As a result, users have access to all their underlying data without any data movement and can leverage native database functionalities. The semantic modelling layer allows standard business logic to be defined across the organization and for data permissions to be set at the row or column level. Being cloud-native and web-based, Looker is very light and provides complete API extensibility. The solution empowers users to consume data in their day-to-day work, be it in analytic format, surveys, or reports in dashboards or other tools (Slack or email). Using Looker, users can even send data to AI or Machine Learning workflows or access it from within data science tools such as R or Python. Migrating to BigQuery with Adastra Migrating to the Cloud is an intensive exercise. As an official Google partner with decades of industry experience, Adastra can help you not only migrate to BigQuery but also modernize your entire data warehousing solution. Our tried-and-tested Migration Framework has enabled hundreds of organizations to migrate on time, without error, and in a cost-effective manner. Before migration, Adastra’s experts will gather your business requirements and undertake end-to-end source data profiling to determine your organization’s data readiness. We will also assess data quality, existing standards, and transformation processes to support a build-out of the new data mapping. Any data quality issues that are found will be alleviated through source data cleansing to help better prepare your data for migration. Adastra’s Migration Framework is broken down into 3 phases: 1) Planning and Initiation, 2) Implementation and 3) Support and Transition. In the first phase, our experts will get a better understanding of your use cases, workloads, and expected end state. Phase 2 starts with defining systems, domains, data, processes, functional scope, and target system structures. Our team will then design source, staging, and landing models, mapping specifications, etc. The next step is building data ingestion and extraction routines, along with application and business process migration.