Cloud-native Data Warehousing

BigQuery is Google's serverless, highly scalable, enterprise data warehouse designed to make all your data analysts productive at an unmatched price-performance. Because there is no infrastructure to manage, you can focus on analyzing data to find meaningful insights using familiar SQL without the need for a database administrator. Analyze all your data by creating a logical data warehouse over managed, columnar storage, as well as data from object storage and spreadsheets. Build and operationalize machine learning solutions with simple SQL. Easily and securely share insights within your organization and beyond as datasets, queries, spreadsheets, and reports. BigQuery allows organizations to capture and analyze data in real time using its powerful streaming ingestion capability so that your insights are always current, and it's free for up to 1 TB of data analyzed each month and 10 GB of data stored.

Data Lake

A data lake offers organizations like yours the flexibility to capture every aspect of your business operations in data form. Over time, this data can accumulate into the petabytes or even exabytes, but with the separation of storage and compute, it's now more economical than ever to store all of this data. After capturing and storing the data, you can apply a variety of processing techniques to extract insights from it. Data warehousing has been the standard approach to doing business analytics. However, this approach requires fairly rigid schemas for well-understood types of data, such as orders, order details, and inventory. Analytics that are built solely on traditional data warehousing make it challenging to deal with data that doesn't conform to a well-defined schema, because that data is often discarded and lost forever. Moving from data warehousing to the "store everything" approach of a data lake is useful only if it's still possible to extract insights from all of the data. Data scientists, engineers, and analysts often want to use the analytics tools of their choice to process and analyze data in the lake. In addition, the lake must support the ingestion of vast amounts of data from multiple data sources.

Data processing with Apache Hadoop and Apache Spark

Cloud Dataproc is a fast, easy-to-use, fully-managed cloud service for running Apache Spark and Apache Hadoop clusters in a simpler, more cost-efficient way. Operations that used to take hours or days take seconds or minutes instead, and you pay only for the resources you use (with per-second billing). Cloud Dataproc also easily integrates with other Google Cloud Platform (GCP) services, giving you a powerful and complete platform for data processing, analytics and machine learning.

Machine Learning and Cloud AI

With enterprise AI on the rise, speed and agility are crucial to keeping competitive, yet custom solutions can be time consuming, complex, and costly. With Google Cloud AI solutions, you can quickly and easily apply solutions across your work streams or combine our technology with vendors you already work with. Whether you're looking to classify images and videos automatically or deliver recommendations based on user data, you can use Google Cloud AI Solutions to drive insights and improve customer experiences.

Stream Analytics

Stream analytics has emerged as a simpler, faster alternative to batch ETL for getting maximum value from user-interaction events and application and machine logs. Ingesting, processing, and analyzing these data streams quickly and efficiently is critical in fraud detection, clickstream analysis, and online recommendations, among many examples. For such use cases, Google Cloud offers an integrated and open stream analytics solution that is easy to adopt, scale, and manage.