ALiZ Team | February 17, 2022
3 Minutes Read
Data is absolutely everywhere and it is quickly becoming a highly valuable commodity in today's digital age. Google is known as the leader in data analytics and its business has mobilized the power of data, mastering search engine queries over the last 20-plus years. Our customers believe it's a natural progression to adopt Google Cloud for Data Analytics, a platform that provides cloud-native analytics designed for streaming and batch processing at scale.
One of the biggest problems business leaders face is how to effectively use the collected data. Data is often unstructured and there are specific challenges over compliance; GDPR for data held on European citizens and CCPA citizens from the United States. GCP helps users make sense of data throughout the whole data lifecycle, unlocking the power of big data, reimagining their business, and successfully navigating the minefield of data privacy.
All data requires a source, and practically all GCP services generate data that can be ingested. GCP works seamlessly with various sources, systems such as Databases, CRMs, and Marketing Tools like Salesforce. Quite often these data sources are hosted outside of GCP. Messaging tools stream events at a breakneck speed, using intelligent decision-making to process the data, and there are numerous 3rd party tools that integrate with GCP, such as Confluent and Fivetran.
Streamed data is typically written to Google Cloud Storage (GCS), this global service is extremely efficient and highly redundant. Cloud Storage is available in numerous flavors, standard (hot) storage, nearline storage, coldline storage, and archive (cold) storage for data rarely accessed. If the source data is structured, it can be directly ingested into BigQuery and can also be feeded directly into ML services like the Cloud Vision API and Cloud Natural Language API.
Process and Analyze
To make data work for you, it must be transformed and analyzed. When processing large-scale datasets, GCP automatically scales the resources required to process huge volumes of data using services such as Cloud Dataproc, Cloud Dataflow, and Cloud Dataprep. Dataproc is perfect for Hadoop or Apache Spark applications feeding data into ML and Data Science ecosystems. Dataflow works seamlessly with unified streaming and batch processing. Dataprep is a UI-Driven data preparation tool that scales automatically. Each is a fully managed service, allowing users to concentrate on analysis.
Explore and Visualize
The final stage of the data lifecycle is using GCP tools to help you make sense of data. Exploring data visually is key for spotting trends and portraying critical messages to decision-makers.
The Vertex AI Workbench is now the standard GCP product used to visualize data in a user-friendly web interface and to access and explore data in a Jupyter notebook. Notebooks support Python and data can be exported to almost any endpoint. GCP’s Looker is a great tool for BI platforms that integrates with the BigQuery BI Engine for live queries against BigQuery datasets. Google Data Studio can visualize data with numerous predefined settings and visualized examples.
Smart Analytics On GCP
Smart Analytics creates real business value in today's connected world and Smart Analytics provides business solutions for everyday challenges.
Real-time data analytics is hugely popular in eCommerce applications that track clickstream data. Harnessing this information provides a unique insight into customer habits and shopping preferences, then it is embedded into Intelligent marketing to target users and maximize sales. Financial services rely on streaming to predict fraud and to build customer profiles about spending habits, it can also be used to monitor trends on the trading floor.
GCP provides real-time data processing with Pub/Sub, a highly scalable service able to stream hundreds of millions of events per second from any source, and with BigQuery’s streaming API millions of events can be pumped into your data warehouse. The service integrates with existing tools like Apache Kafka, Spark, or Beam.
Geospatial Analytics & AI
Geographic Information System (GIS) data is all about imagery from GPS, satellite photography, and maps. Geospatial Analytics focus on identifiers around location information (such as a customer address). Businesses reliant on global logistics benefit greatly from this technology through efficient and greener route planning.
GIS data is used in urban planning, predicting telecommunications signal strengths, and even natural resource exploration. As businesses diversify into much greener entities, AI can offer insights into achieving sustainability. Google Earth and Google Maps are just two services built on GIS data and it is BigQuery that powers the data analytics using standard SQL.
We have just scratched the surface on how GCP smart analytics can improve business intelligence through analytics and AI.
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