Sharing and exchanging data with other organizations is a critical element of any organization’s analytics strategy. In fact, BigQuery customers are already sharing data using our existing infrastructure, with over 4,500 customers swapping data across organizational boundaries. Creating seamless access to analytics workflows and insights has become that much easier with the introduction of Analytics Hub and surfacing datasets unique to Google.

Apache Spark has become a popular platform as it can serve all of data engineering, data exploration, and machine learning use cases. However, Spark still requires the on-premises way of managing clusters and tuning infrastructure for each job.

The first step in most analytical workloads is to ingest the data that you need for your analysis into your data warehouse. For geospatial analysis involving point, line, or polygon data, ingesting data can be complex because geospatial data comes in myriad data formats. Two of the most popular geospatial formats are GeoJSON and GeoJSON-NL (newline-delimited geoJSON).

As businesses scale to meet the demands of their customers, so do their need for efficient products to collect, manage and analyze data to meet their business goals. Whether you are building a multi-player game or a global e-commerce platform, it's critical to ensure that data can be stored and queried at scale with strong consistency and then processed for analysis to deliver real-time insights.

Geographical data is one of the critical datasets for data-driven organizations to make informed business decisions. As the data is growing more than ever before, it’s becoming more challenging to manage and analyze mammoth datasets using traditional databases, this is true for geographical data as well as it requires significant computational power to process. Esri has been one of the leading companies in Geospatial software development since 1969.

The Google BigQuery Write API offers high-performance batching and streaming in one unified API. The previous post in this series introduced the BigQuery Write API. In this post, we'll show how to stream JSON data to BigQuery by using the Java client library.