As we become increasingly reliant on technology to make decisions, geospatial data is becoming more critical than ever. It is a powerful resource that can be used to solve a variety of problems, from tracking the movement of goods, identifying interest areas and potential areas of disaster.

Geospatial data incorporates data with a geographic component, such as latitude and longitude coordinates, addresses, postal codes, or place names, and can be obtained from a variety of sources, including satellites, sensors, and surveys, making it an incredibly powerful tool with a broad range of applications.

One of the key features of BigQuery, Google Cloud’s serverless enterprise data warehouse, is its ability to analyze geospatial data. However, oftentimes geospatial data sits in a variety of public clouds, not just Google Cloud. To access it effectively, you need a multi-cloud analytics solution that lets you capitalize on the distinct capabilities of each cloud platform, while extracting insights and value from data sitting across multiple cloud platforms.

BigQuery Omni is a multi-cloud analytics solution that enables the analysis of data stored across public cloud environments, including Google Cloud, Amazon Web Services (AWS) and Microsoft Azure, without the need to transfer the data. With BigQuery Omni, users can employ the same SQL queries and tools used to analyze data in Google Cloud to analyze data in other clouds, making it easier to gain insights from all data, regardless of the storage location. For businesses using multiple clouds, BigQuery Omni is an excellent tool to unify analytics and optimize the value of data.

With BigQuery Omni, organizations can analyze location-based information or geographic components, such as latitude and longitude coordinates, addresses, postal codes, or place names without even copying their data to Google Cloud. For example, you could use BigQuery Omni to analyze data from a fleet of delivery vehicles to track their location and identify potential problems.

BigQuery Omni and geospatial data analysis

If you are working with geospatial data, BigQuery Omni is a powerful tool that can help you to get insights from your data. It is scalable, reliable, and secure, making it a great option for unifying your analytics and getting the most out of your data. 

Here are a few examples of ways organizations might use BigQuery Omni for geospatial data:

• A transportation company could use BigQuery Omni to analyze data from GPS sensors in its vehicles to track the movement of its fleet and identify potential problems.
• A retail company could use BigQuery Omni to analyze data from its point-of-sale systems to track customer behavior and identify trends.
• A government agency could use BigQuery Omni to analyze data from its weather sensors to track the movement of storms and identify areas at risk of flooding.

BigQuery Omni and geospatial data can be used together to gain insights into a variety of business problems. Specifically, some of the advantages of using BigQuery Omni and geospatial data include:

• Access to quality geospatial data: BigQuery supports loading of newline-delimited GeoJSON files and provides built-in support for loading and querying geospatial data. Data from public data sources like BigQuery public datasets, the Earth Engine catalog, and the United States Geological Survey (USGS) can be easily integrated into your BigQuery environment. Earth Engine has an integrated data catalog with a comprehensive collection of analysis-ready datasets, including satellite imagery and climate data. This data can be combined with proprietary data sources such as SAP, Oracle, Esri ArcGIS Server, Carto, and QGIS.
• Loading and preprocessing of geospatial data: BigQuery has built-in support for loading and querying geospatial data types, and you can use partner solutions such as FME Spatial ETL to load data.
• Working with different geospatial data types and formats: BigQuery supports a variety of file types and formats including WKT, WKB, CSV and GeoJSON.
• Coordinate reference systems: BigQuery’s geography data type is globally consistent. That means that your data is registered to the WGS84 reference system and your analyses can span a city block or multiple continents.

Overall, geospatial analytics with BigQuery Omni provides a wide range of technical capabilities for processing and analyzing geospatial data, making it a powerful tool for businesses that need to work with location-based data.

Analyzing geospatial data with BigQuery Omni

Imagine a retailer who has a large chain of department stores with locations all over the country. They are looking to expand their business and want to identify areas with high sales potential. They want a way to get a better understanding of their sales volume within specific geographic boundaries. To achieve this goal, the retailer turns to the GIS (Geographic Information System) functions built into BigQuery. Here are the steps what the retailer takes to analyze this dataset:

Step 1 : An initial orders dataset on AWS S3 contains 5.54 million rows, and with separate locations (300 rows) and zipcode (33144 rows) metadata files on AWS S3.

Orders Parquet files:

Location and Zipcode files:

Step  2 : The retailer uses BigQuery Omni to establish a connection between the data stored in AWS and BigQuery, enabling them to access the S3 datasets externally.

External Connection for AWS

External Table for orders

External Table for locations

External Table for zipcode

Step 3 : They combine the orders and locations datasets using BigQuery Omni, and remotely aggregate the data on AWS. Joining this dataset with geospatial datasets helps them derive geospatial coordinates.

The final aggregated dataset is reduced to 23 rows. Subsequently, they bring back the result dataset, which contains just 23 rows. This helps them reduce the extraction of millions of rows to just 23 rows for their geospatial analytics.

Select sales.store_city store_city,
sales.number_of_sales_last_10_mins number_of_sales_last_10_mins,
sales.store_zip store_zip,
 ST_GeogPoint(zip_lat_lng.longitude ,
   zip_lat_lng.latitude  ) geo
FROM (
 SELECT
   FORMAT_DATETIME("%X",
     CURRENT_DATETIME("America/Los_Angeles")) current_time,
   MAX(DATETIME(time_of_sale,
       "America/Los_Angeles")) time_of_last_sale,
   COUNT(1) number_of_sales_last_10_mins,
   locations.city store_city,
   locations.zip store_zip
 FROM
   `bqomni-blog.aws_locations.orders_small`  sales
 JOIN
   `bqomni-blog.aws_locations.locations` locations
 ON
   sales.store_id = locations.id
 GROUP BY
   locations.city,
   locations.zip ) sales
JOIN
   `bqomni-blog.aws_locations.zipcode` zip_lat_lng
ON
 cast(sales.store_zip as INT) = cast(zip_lat_lng.zipcode as INT)
 WHERE ST_WITHIN( ST_GeogPoint(zip_lat_lng.longitude , zip_lat_lng.latitude  ) ,ST_GeogFromText(zip_lat_lng.zipcode_geom ) )
 AND zip_lat_lng.state_name  = "New York"
 ORDER BY number_of_sales_last_10_mins

Aggregated Sales data by region

To build richer views of their sales volume data, the retailer uses BigQuery GeoViz integration, a powerful tool that allows for visualizing geographic data on maps.BigQuery Geo Viz is a web tool for visualization of geospatial data in BigQuery using Google Maps APIs. You can run a SQL query and display the results on an interactive map

BigQuery Geo view for sales data analysis

With the geo-tagged data in place, the retailer can now see regional sales volume, sales density, distribution by department by time, and distribution within department, all powered through BigQuery.

Satellite view

Benefits of using BigQuery Omni 

Beyond geospatial analysis, BigQuery Omni offers a number of benefits, including:

Reduced costs: BigQuery Omni’s ability to eliminate data transfers between clouds can help organizations reduce costs and simplify data management, making it a valuable tool for multi-cloud analytics. Also, the ability to access and analyze data across multiple clouds can reduce the need for data replication and synchronization, which can further simplify the ETL process and improve data consistency.

Unified governance: BigQuery Omni uses the same security controls as BigQuery, which include features such as encryption, access controls, and audit logs, to help protect data from unauthorized access.

Single pane for analytics: BigQuery Omni provides a single interface for querying data across all three clouds, which can simplify the process of analyzing data and reduce the need for organizations to use multiple analytics tools.

Flexibility: Analyze data stored in any of the supported cloud storage services, giving organizations the flexibility to work with the data they have regardless of where it’s located.

BigQuery Omni is a valuable tool for geospatial analysis because it allows you to analyze data from multiple sources without having to move the data. This can save you time and money, and it can also help you to get more accurate insights from your data.If you are looking for a way to improve the accuracy, efficiency, and decision-making of your business, using BigQuery Omni to analyze geospatial data can be a powerful tool.

References

• Omni-introduction 
• Omni-benefits 
• Omni-aws-create-connection
• Omni-azure-create-connection 
• BigQuery Omni Retail Demo on Looker
• Omni-pricing 
• Working with Geospatial Data
• Geospatial Analytics – Intro

Learn more about how BigQuery Omni can help your organization.

Today, we are excited to announce the general availability of Dataplex data lineage — a fully managed Dataplex capability that helps you understand how data is sourced and transformed within the organization. Dataplex data lineage automatically tracks data movement across BigQuery, BigLake, Cloud Data Fusion (Preview), and Cloud Composer (Preview), eliminating operational hassles around manual curation of lineage metadata. 

With rising data volume spread across data silos, it can be challenging for organizations to ensure users have a self-service mechanism to discover, understand and trust the data. Organizations constantly struggle with questions such as:

• Is the data extracted from an authoritative source?
• What is the impact if I drop this table?
• The data in this table seems corrupted – where did this data come from, and when was it last refreshed?
• How is sensitive information being moved or copied? Is it in adherence to data governance practices?

To answer the above questions, organizations need to track how data is sourced and transformed, which can be complex and requires significant effort.

Dataplex data lineage describes each lineage relationship by detailing what happened and when it happened in an interactable lineage graph, providing data observability.

Data analysts who want to know if a table originates from an authoritative source can now answer this in a self-service manner with a simple look-up of lineage for the concerned table — available in Dataplex and in BigQuery for in-context analysis. 

Data engineers can reduce time to identify and resolve data issues through root cause analysis using the operational metadata trace asserting a lineage relationship. Data lineage also aids deterministic change management by providing the ability to evaluate the impact of a change and collaborate with the corresponding stakeholders to minimize any adverse impact. 

Finally, data lineage provides a map of data movement which can become the foundation for data governance practice. It enables data stewards and owners to evaluate and enforce adherence to governance requirements, especially when tracking the movement of sensitive information. 

Dataplex data lineage provides APIs for extensibility so that organizations can report lineage from various systems and have a single map of how data entries are related.

What our customers are saying

L’Oréal, the world’s largest cosmetics company, is on a mission to ‘create the beauty that moves the world.’ “Dataplex data lineage helps us understand how data moves across our organization,” said Sébastien Morand, Head of Data Engineering team, L’Oréal. “As a fully managed solution, it becomes the main entry point to diagnose data issues and evaluate the impact of a change or incident — providing insight on what happened and when it happened, including reference to the execution metadata. Directly integrated into our beauty tech data platform, data lineage helps us reduce data issues and also enables us to mitigate issues faster when it does happen.” 

“At Wayfair, we treat data-as-a-product and are building a robust data platform that provides self-service access and compliance constructs,” said Vinit Rajopadhye, Associate Director on Data Infrastructure & Data Enablement at Wayfair. “We are excited about Dataplex data lineage as it helps our data consumers trust data based on where it originates and the transformations applied.”

Hurb is an online travel agency in Brazil with a mission to optimize travel through technology. “Hurb has a rapidly growing data platform, with new data assets created and registered daily to support business decision-making and Machine Learning models,” said Vinícius dos Santos Mello, Senior Data Engineer. “Thanks to Dataplex data lineage features, we have end-to-end data observability across data in BigQuery. We can proactively address schema changes, data quality issues, and asset depreciation that could otherwise negatively affect the business.”

“As a company with many business domains and services, we handle a large volume of  data and use it to power our decision making, so it is crucial to ensure data quality. Dataplex data lineage provides a visual understanding of the flow of data across our organization, improving efficiency of impact investigations when problems occur and increasing the reliability of the data.” said Mitsunori Fukase, Data Platform Department Group Manager, DeNA.

Get started with Dataplex data lineage

You can get started with Dataplex data lineage by enabling the Data Lineage API on your project. You can learn more here.

Additional Resources:

• Dataplex data lineage labs
• Quickstart – track lineage for BigQuery table copy

As a leading French agricultural cooperative, Groupe Dauphinoise places collaboration at the heart of its philosophy. Working with farmers in the Rhone-Alpes region, Groupe Dauphinoise takes on a diverse range of activities from agricultural production to research and development to running retail outlets.

As its operations expanded and strained its existing infrastructure, Groupe Dauphinoise saw the opportunity to upgrade its technology solutions and adopted G Suite, Chrome devices and ultimately Google Cloud Platform (GCP).

“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream,” says Sylvain Claudel, Head of IT at Groupe Dauphinoise.

“We transitioned to G Suite to improve our staff’s collaboration. We quickly realized that as the company grew, we needed a new infrastructure. Based on our satisfaction with G Suite, we chose GCP. With Google, ‘any device, anytime, anywhere’ is not just a dream.”

—Sylvain Claudel, Head of IT, Groupe Dauphinoise

Flexible workforce, stable infrastructure

Many of Groupe Dauphinoise’s 1,500 employees spend their days out of the office. Five years ago, with the help of Google partner GoWizYou, the company transitioned to G Suite to improve the flexibility and collaboration of its staff.

As Groupe Dauphinoise began to expand and collect more data, the company reached the limits of its on-premise infrastructure. Adding new storage was not a simple matter. Acquiring, configuring and synchronising new servers costs Groupe Dauphinoise time as well as money. In addition, with all its servers stored in a single room, security was a concern. Groupe Dauphinoise needed a new infrastructure.

Google Cloud Platform was the only solution in mind after Groupe Dauphinoise’s experience with G Suite and Chromebooks. Disruption was kept to a minimum thanks to Google’s licensing agreements with Microsoft products, allowing the company to migrate without affecting its operations.

After migrating its infrastructure to Compute Engine, Groupe Dauphinoise can let Google look after the security and maintenance. Cloud IAM makes it easy for Groupe Dauphinoise to hand out permissions to sensitive resources across a number of sites with maximum security and minimum fuss. The company placed its archives in Cloud Storage, while Cloud SQL allows it to continue to make use of its MySQL databases without disrupting the day to day business. Meanwhile, BigQuery provides Groupe Dauphinoise with the raw power to analyse large datasets quickly.

“Our company is growing and we have more and more data to collect and analyze like sales data, weather patterns or production numbers. With GCP, we have more than a single on-premise data store, so our disaster recovery plan is much more flexible. Compute Engine allows us to add more storage quickly and easily, without having to spend days synchronizing data and installing new servers. We have improved security and maintained the stability of our infrastructure while keeping costs down,” says Sylvain.

Safeguarding the present, looking to the future

With Google Cloud Platform, Groupe Dauphinoise has expanded and secured its infrastructure without sinking costs into on-premise servers or DevOps staff. Its investment in Chrome devices and adoption of G Suite mean that its mobile workforce can fully reap the benefits of a cloud-based infrastructure while dramatically cutting the cost of hardware. Meanwhile, working with a 200 million line table of sales data in Google BigQuery, the cooperative found that queries ran ten times faster than with its previous database provider. Groupe Dauphinoise is experimenting with Google BigQuery to expand its BI capabilities. Products like Google BigQuery help Groupe Dauphinoise grow its business, safe in the knowledge that its infrastructure is stable and secure.

“The amount of data we collect is growing very quickly. We need to break our rules and evolve from the mindset that we had with on-premise infrastructure and our old databases. We can now look at collecting more customer fidelity data, or big data for our farmers. With our data and infrastructure in Google’s care, we can concentrate on growing our customer base instead of our IT department!” says Sylvain.