5 Ways to Transform Your Big Data Into Big Insights

SADA Says | Cloud Computing Blog

By SADA Says | Cloud Computing Blog

Deriving actionable business intelligence from millions or billions of data assets can be an onerous task, particularly since about 80% to 90% of organizational data is unstructured1. Without the right tools, big data is nothing but a big pile of unrelated information that would take years for human analysts to scratch the surface of, with no guarantee of yielding any insights.

Google Cloud’s fully managed, serverless smart analytics enable organizations to transform their big data into smart data without having to manage the underlying infrastructure or overcome scaling, performance, or cost constraints. Here are five ways your organization can use Google Cloud smart analytics to unlock valuable insights and make data-driven decisions: 

1. Simplify Data Migration and Integration With Cloud Data Fusion

In many organizations, massive amounts of data are stored in siloed, incompatible systems, all of which must be migrated to the cloud, then integrated, before analysis can commence. 

Cloud Data Fusion is a fully managed, cloud-native data integration service that greatly simplifies this process and gives users a central control center where all data pipelines and data sets can be managed and explored. Cloud Data Fusion’s extensive open-source library of preconfigured connectors and transformations support a wide variety of data sources and formats, making it easy to ingest and integrate data from various sources and transform it for analysis. Users deploy ETL data pipelines through a simple, no-code point-and-click graphical interface, making data prep a snap.

2. Use BigQuery for Simple but Smart Analytics at Scale

Traditional data warehouse solutions are expensive and extremely difficult to operate and manage. In fact, organizations that use these legacy systems end up spending about 85% of their time on systems engineering tasks instead of data analysis.2

BigQuery, Google’s serverless warehousing solution, eliminates administrative burdens so that organizations can get down to business and derive insights from their data, with  automatic resource provisioning for faster execution of queries. BigQuery’s user-friendly graphical interface and seamless integration with popular business intelligence (BI) tools allow anyone to create crisp, professional dashboards and reports and securely share them. BigQuery also enhances data security with built-in encryption, automatic data protection, and data replication at scale. Meanwhile, administrators can access Google Cloud’s identity and access management (IAM) tools to control and automate access to encrypted datasets.

By eliminating administrative overhead and enabling analysts to get answers faster, BigQuery directly benefits organizations’ bottom lines. Enterprise Strategy Group found BigQuery’s three-year TCO to be 26% to 34% lower than cloud data warehouse alternatives3.

3. Respond to Changes in Real Time With Streaming Analytics

Real-time streaming analytics allow organizations to immediately respond to customer sentiment, personalize user experiences, detect fraud, and fine-tune app features.

With Google Cloud’s stream analytics tools, organizations can glean insights from large-scale real-time data streams at the moment they are generated. Google’s streaming solution leverages the autoscaling capabilities of its core components—Cloud Pub/Sub, Cloud Dataflow, and BigQuery—to automate and abstract resource provisioning, which makes streaming analytics accessible to both data analysts and data engineers. 

Cloud Dataflow is a fully managed, unified batch processing and streaming analytics service offering serverless, non-SQL imperative, or functional big data processing of streams using an implementation that can easily integrate with batch workloads4. Dataflow automates provisioning and management of processing resources to minimize latency and maximize utilization, and it automates and optimizes work partitioning to dynamically rebalance lagging work, ensuring optimum throughput results. By separating compute from state storage, Dataflow allows users to deploy more responsive, efficient, and supportable streaming pipelines.

Through integration with Confluent Cloud and Cloud Dataproc, organizations can even keep using their existing on-prem and cloud streaming solutions, such as Apache Kafka and Apache Spark, while exploring Google Cloud’s next-generation analytics tools. 

It’s no wonder Forrester recognized Google Cloud as a leader in their 2019 streaming data analytics wave report5.

4. Accelerate Time to Insights With BigQuery BI Engine & Connected Sheets

The path to critical organizational insights is often bottlenecked because data analysts and other non-technical employees may not know how to code. Google Cloud smart analytics democratizes data analysis by giving non-technical employees the tools they need to perform enterprise-class analytics at scale.

Data analysts and other business users often analyze data from data warehouses using BI reports and dashboards. BigQuery BI Engine, currently available in beta, integrates with Data Studio, Google’s fully managed visual analytics service. This minimizes the need for additional ETL or BI server management and accelerates time to insight. BigQuery BI Engine allows users to interactively analyze complex data sets, with fast query response time and high concurrency, then generate interactive reports and dashboards in Data Studio. In the coming months, BigQuery BI Engine will be integrated with Google Sheets, as well as familiar BI tools from Google partners including Looker and Tableau.

What about users who don’t know how to use BigQuery? No problem! Anyone who knows how to use a spreadsheet can harness the power of BigQuery thanks to the new Connected Sheets feature, now available in beta. With Connected Sheets, users can import and analyze up to 10 billion rows of BigQuery data right from within Google Sheets using standard pivot tables, charts, and functions — with no SQL knowledge or BigQuery expertise necessary.

5. Run Workloads on Open Source Tools

Enterprises are increasingly turning to open source solutions so that they can modify the software to suit their specific business needs, avoid vendor lock-in, and reduce their upfront costs. Google Cloud Platform (GCP) has an open architecture and seamlessly integrates with popular open source tools, including Apache Hadoop and Spark. GCP users can reduce their costs and enhance performance by moving their Apache Spark and Hadoop workloads to Cloud Dataproc, Google’s fully managed service for running Apache Spark and Apache Hadoop clusters. Data pipelines built in GCP using the open source Apache Beam SDK work on numerous open source runtimes.

  1. https://www.webopedia.com/TERM/U/unstructured_data.html
  2. https://cloud.google.com/blog/products/data-analytics/5-reasons-your-legacy-data-warehouse-wont-cut-it
  3. https://services.google.com/fh/files/blogs/esg_economic_validation_google_bigquery_vs_cloud-based-edws-september_2019.pdf
  4. https://cloud.google.com/dataflow
  5. https://cloud.google.com/blog/products/gcp/google-cloud-named-a-leader-in-the-forrester-wave-streaming-analytics


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