A data lake that powers any analysis on any data
Google Cloud’s data lake empowers data professionals to securely and cost-effectively ingest, store, and analyze large volumes of diverse, full-fidelity data. Building your data lake on Google Cloud’s auto-scaling services allows you to decouple storage from compute to increase query speeds and manage cost at a per-gigabyte level.
Migrated >12,000 on-premises Hadoop nodes to GCP
Massive TCO savings from moving off of on-premises Apache Hadoop
Re-host your data lake on Google Cloud
If you’ve already invested time into your on-premises data lake but you don’t want to rebuild from scratch in the cloud, lift and shift your data and code to Google Cloud to unlock cloud cost savings and scale.
Burst a data lake workload to Google Cloud
Take a resource-intensive data or analytic processing workload (ETL, SQL, model training, etc.) and burst it to the cloud, so you can autoscale compute without provisioning new hardware. Keep the workload running in the cloud or simply end the cluster after the peak has passed.
Build a cloud-native data lake on Google Cloud
If your data lake turned into a swamp and you want to rethink your plan, build a cloud-native data lake on Google Cloud to help your data engineers, data analysts, and data scientists accelerate development of analytics.
Fully managed services
Google Cloud’s fully managed services remove the complexities associated with managing physical hardware. This means you can provision and autoscale clusters for easier management in as little as 90 seconds, and almost instantly spin up new resources and services.Learn about Apache Hadoop and Spark
Accelerate data and analytics processing
Decrease your data and analytics processing times from hours to seconds by scaling compute independent of storage. You can also power real-time applications, train large scale models, and execute burst queries in seconds, without having to provision more hardware.Read about Pandora’s move to the Cloud
On average, Google Cloud TCO is 57% lower than an on-premises Hadoop deployment. A serverless data lake will allow your IT team to scale efficiently without worrying about software upgrades and physical hardware.Get the detailed ESG report
Scale AI and machine learning
From our cloud services to the hardware they run on, we’ve optimized the entire stack to scale machine learning. This means that building your data lake on Google Cloud lets you quickly add ML and AI services to power your current and future analytic use cases.Learn about our AI and ML products
Secure and governed
From the chip to the user, we ensure that any and all data is protected, so that you can secure your data, integrate with key data governance solutions, and meet the strictest compliance regulations.Read about our Titan chip design
Take a look at the main Google Cloud services used in data lake migrations.
Migrating Apache Hadoop or Spark workloads? Spin up a Dataproc cluster in seconds and copy/paste your existing Spark code.