When your data is siloed data across lakes and warehouses, it can be hard to transform outcomes with your data. Apache Iceberg is an open table format that provides data management capabilities for data hosted on object stores and enables organizations to run analytics and AI use cases over a single copy of data. A growing community of data engineers, customers, and industry partners are contributing, integrating, and deploying Iceberg, making it the standard for organizations building open-format lakehouses. 

To help customers on this journey, we announced support for Iceberg through BigLake in October, 2022. Since its preview, many customers have started building lakehouse workloads using Apache Iceberg as their data management layer, and this support is now generally available. 

Unify analytics, streaming and AI use cases over a single copy of data

You can use open-source engines to process and ingest data into Iceberg tables, and BigQuery can query those tables. Since the preview, customers have also used Spark, Trino and Flink to process Iceberg tables and make those tables available to their BigQuery users. Then, BigLake Metastore provides shared metadata for Iceberg tables across BigQuery and open-source engines, eliminating the need to maintain multiple table definitions. Further, you can provide BigQuery datasets and table properties when creating new Iceberg tables in Spark, and those tables become automatically available for the BigQuery user to query.

When implementing Iceberg lakehouse workloads, query performance is a top priority for data warehouse users. BigQuery natively integrates with the Iceberg transaction logs and leverages its rich metadata for efficient query planning. Query plans are designed to reduce BigQuery compute consumption by lowering the amount of scanned data, by optimizing joins, and by improving data-plane parallelism. The net result is that you get better query performance and lower slot usage when querying BigLake Iceberg tables. 

This GA release also adds support to provide automatic synchronization of table schema in BigQuery when the table is modified through an open-source engine. 

Engine-agnostic, industry-leading security and governance built-in

Customers have been telling us that building Iceberg lakehouses in a secure and governed manner is a top priority. BigLake support for Iceberg provides fine-grained access control, including row- and column-level security as well as data masking to simplify this. These features are designed to work independently of the query engine. During the preview, we expanded BigQuery to also support differential privacy for all tables including Iceberg. 

You can also define security policies on a BigLake Iceberg table using BigQuery. Security policies are then enforced regardless of the query engine used — BigQuery natively enforces these policies at runtime, and open-source engines can securely access the data using the BigQuery Storage API. The BigQuery Storage API enforces the security policies at the data-plane layer, and is offered via pre-built connectors for Spark, Trino, Presto and TensorFlow. You can also use client libraries to build connectors for custom applications. 

New use cases with multi-cloud Iceberg lakehouse

The open nature of Apache Iceberg lets you build multi-cloud lakehouses with uniform management of data. With this launch, you can now create BigLake Iceberg tables on Amazon S3, and query them using BigQuery Omni. BigLake’s performance and fine-grained access control features seamlessly extend to multi-cloud Iceberg tables, so you can securely perform cross-cloud analytics with BigQuery. We’ll extend similar support to Azure data lake Gen 2 in the coming weeks. 

Apache Iceberg’s format uniformity across clouds also enables new data sharing use cases to help you share data with your customers, partners and suppliers, regardless of which cloud they are using. BigLake Iceberg tables on Cloud Storage or on Amazon S3 can be shared through Analytics Hub and consumed via BigQuery or OSS engines via BigQuery Storage Read API, providing an open sharing standard and the flexibility to use multiple query engines. A notable example of this is the recently announced Salesforce Data Cloud data sharing use case, which enables bi-directional cross-cloud data sharing between Salesforce and BigQuery and which is powered by Iceberg. 

Getting started

To create and query your first Iceberg table, following the documentation. or run a quick proof of concept on our analytics lakehouse with the Iceberg jump start solution.

Over the past few decades, organizations have mastered the science of data warehousing. They have increasingly applied descriptive analytics to large quantities of stored data, gaining insight into their core business operations. Conventional Business Intelligence (BI), which focuses on querying, reporting, and Online Analytical Processing, might have been a differentiating factor in the past, either making or breaking a company, but it’s no longer sufficient.

Today, not only do organizations need to understand past events using descriptive analytics, they need predictive analytics, which often uses machine learning (ML) to extract data patterns and make probabilistic claims about the future. The ultimate goal is to develop prescriptive analytics that combine lessons from the past with predictions about the future to automatically guide real-time actions.

Traditional data warehouse practices capture raw data from various sources, which are often Online Transactional Processing (OLTP) systems. Then, a subset of data is extracted in batches, transformed based on a defined schema, and loaded into the data warehouse. Because traditional data warehouses capture a subset of data in batches and store data based on rigid schemas, they are unsuitable for handling real-time analysis or responding to spontaneous queries. Google designed BigQuery in part in response to these inherent limitations.

Innovative ideas are often slowed by the size and complexity of the IT organization that implements and maintains these traditional data warehouses. It can take years and substantial investment to build a scalable, highly available, and secure data warehouse architecture. BigQuery offers sophisticated software as a service (SaaS) technology that can be used for serverless data warehouse operations. This lets you focus on advancing your core business while delegating infrastructure maintenance and platform development to Google Cloud.

BigQuery offers access to structured data storage, processing, and analytics that’s scalable, flexible, and cost effective. These characteristics are essential when your data volumes are growing exponentially—to make storage and processing resources available as needed, as well as to get value from that data. Furthermore, for organizations that are just starting with big data analytics and machine learning, and that want to avoid the potential complexities of on-premises big data systems, BigQuery offers a pay-as-you-go way to experiment with managed services.

With BigQuery, you can find answers to previously intractable problems, apply machine learning to discover emerging data patterns, and test new hypotheses. As a result, you have timely insight into how your business is performing, which enables you to modify processes for better results. In addition, the end user’s experience is often enriched with relevant insights gleaned from big data analysis, as we explain later in this series.

The migration framework

Undertaking a migration can be a complex and lengthy endeavor. Therefore, we recommend adhering to a framework to organize and structure the migration work in phases:

1. Prepare and discover: Prepare for your migration with workload and use case discovery.
2. Assess and plan: Assess and prioritize use cases, define measures of success, and plan your migration.
3. Execute: Iterate the following steps for each use case:
   1. Migrate (offload): Migrate only your data, schema, and downstream business applications.
   2. Migrate (full): Alternatively, migrate the use case fully end-to-end. The same as Migrate (offload), with the addition of the upstream data pipelines.
   3. Verify and validate: Test and validate the migration to assess return on investment.

The following diagram illustrates the recommended framework and shows how the different phases are connected:

For a deeper understanding, read Migrating data warehouses to BigQuery: Introduction and overview

It’s simply amazing to witness how some of our systems integrators employ Google Cloud solutions to drive innovation in ways we at Google may never have considered—especially in healthcare.  According to analyst firm MarketsandMarkets, the market for the Cloud in healthcare is projected to grow 43% between 2020 and 2025 to nearly $65 billion, fueled by the need for better technology infrastructures and faster digital transformation.  

Healthcare and life sciences communities are looking to Google Cloud and its Service engagement model partners to improve collaboration and activate the power of medical data.  These transformations deliver robust data analytics and bring a much deeper perspective into health epidemics like COVID-19 to help save lives.

In the healthcare and life sciences industry, our Google Cloud partners and customers provide constant energy and inspiration–and are the magic in some key healthcare innovations globally.  

Let me show you how they are solving real-world business challenges.

Improving collaboration in Healthcare

Cloudbakers and Comanche County Memorial Hospital transitioned 2,000 employees to Google Workspace to improve collaboration, reduce costs, and increase security. By implementing a system that requires less maintenance while enabling mobile access to data and true collaboration, Comanche County Memorial Hospital saves $175,000 annually on licenses and helps medical professionals spend more time with patients.

“We chose Google Workspace because it cost a quarter of what we were paying previously, offers the kind of modern features that healthcare facilities need, and gave us data security and peace of mind.” —James Wellman, CIO, Comanche County Memorial Hospital

Accessing previously locked down medical data

Google Cloud and Quantiphi supported advances in cloud-based machine learning services to reduce infrastructure costs, unlock new paths of treatment, and dramatically reduce the amount of time it takes to evaluate scanned imagery following a stroke.  John Hopkins University BIOS Division has been working on medical imaging to accelerate insights from scans on approximately 500 patients from 2,500 hours to 90 minutes, and lead to more accurate decision-making for brain injury patients that will ultimately improve medical outcomes.  

“We’ve aligned closely with the goal of showing that a cloud-based, AI-driven approach is robust and that it can be performed while protecting personal PHI. In terms of costs, the cloud has definitely reduced some of the traditional financial demands of our research.” —Daniel F. Hanley, Jr., M.D., Director, Johns Hopkins University BIOS Division

Improving data access with the flexibility of Google Cloud

With the help of MediaAgility, TRIARQ migrated to Google Cloud to modernize their platform, build new applications, and expand their global footprint.  With BigQuery, TRIARQ can now consolidate all transactional data, past and present, into a single location to report on specific insights and predict future data from e-prescriptions to complex revenue-cycle data and value-base analysis.

“The future of this industry will need to be a global one, and we can no longer be stuck in legacy systems if we want to survive. Having a team that is passionate about supporting us in this journey is definitely a plus point.”—Yaw Kwakye, Co-founder and Chief Architect, TRIARQ

Increasing visibility to COVID-19 outbreaks for actionable insights

As part of its response to the COVID-19 pandemic, HCA Healthcare chose to work with Google Cloud and SADA to create a national portal that increases visibility into outbreaks in 3,100 counties across the country in just 8 weeks.  Now, the portal generates 30,000 new analytical views each day that can help inform private and public sector decision making for reopenings, closures, hot spots, and many other population health management activities. 

“This project required deep knowledge of AI, and consumer-facing platforms, as well as healthcare. Google brought together the ideal combination of product, people, and partners. Google Cloud’s healthcare-specific products along with SADA’s expertise in the healthcare IT space made this partnership the perfect choice to move quickly and intelligently.”—Dr. Edmund Jackson, Chief Data Officer, HCA Healthcare

We’re committed to building the technology, resources, and services through Partner Advantage to help our partners address this opportunity.  Looking for a solution focused partner in your region who has achieved Expertise and/or Specialization in your industry?  Search our Global Partner Directory.  Not yet a Google Cloud partner? Visit Partner Advantage and learn how to become one today!