Cloud Analytics enables business in leveraging data analytics with BI processes backed by a mix of cloud services. The highly scalable model of cloud analytics platforms offers advance analytics capabilities and helps reduce the burden of on-premises provisioning & management. With features like a hosted data warehouse, SaaS BI, and social media analytics, it assists in delivering quality services and provides storage for big data.
How to Leverage Data Analytics to Grow your business?
Conversion Rate Optimization
Google AnalyticsG-Suite increases your business revenue and improving key performance metrics, offering a ready-go strategy for capturing and mitigating business challenges.
Modern BI and Analytics
Looker enables companies implementing self-service business intelligence and visual analytics tools that help them access and make sense of new and diverse sources of data.
- Fasten data literacy
- BI and Analytics support
- Data-driven objective
- Examining and confirming user needs
Looker helps companies getting easy access to their sensitive real-time data that delivers fresh and very accurate results giving better reporting.
- Real-time dashboard
- In-depth data analysis
- Single point of data access
BI Security and Governance
Looker offers companies a data-driven platform that helps them scale with the exponential growth of data volumes and its increasing demands.
- Next-level BI security .
- BI governance infrastructure.
- keeps manage data growing needs.
- Security certifications like ISO 27001, PCI and SOC 2 TYPE.
Big Data Analytics
Big Data analytics deliver a fact-based decision that conducts regular experimentation on works.
- Guidance for executive.
- Helps companies in growth.
- Off-culture management strategy.
- Promotion of data-sharing practices.
- Increased availability of training in data analytics.
It provides businesses with an edge over their rivals and makes advanced business decisions.
- Un-tackled traditional data.
- Open-source software framework.
- Integrated with big data analytics.
- Evaluate volumes of transactional data.
- Support Hadoop, MapReduce and NoSQL databases.
- Manages & processes huge data sets over cluster systems.
Clear Business Need
Big Data analytics offers a business-driven project rather than technology-driven that helps create a potential business problem.
- Focus on customer-centric objectives.
- Helps companies understand customers better.
- Uses all accessible internal sources of data.
- Develop meaningful relationships with customers.
- Improve operations to enhance the customer experience
Oracle Cloud Analytics
Extend Insight Consumption
Offered analytics experience that makes it faster and easier for you to consume, socialize, and share contextual insights.
- Scheduled delivery of pixel-perfect reports.
- Enable easy capturing of data from the world.
- Freely available Oracle Data Visualization content packs.
- Personalized and proactive insights keep you updated in real-time.
- Enables quick-start self-service analytics for key roles in SaaS apps.
- Collaboration tools empower you to socialize insights and drive results.
- Conversational interfaces enable you to use voice and search to ask questions.
- Autonomous analytics generating natural language processing of attributes and virtual reality experiences.
Power Deeper Insights
Offered a unique combination of in-depth hidden insights let you ask new questions, and get better answers.
- Easy self-service data preparation and blending.
- Ad hoc analysis and reporting that is fully mobile.
- Automatic visualization of insights and one-click advanced analytics.
- Adaptive user experience to adjust the display depending on the device.
- Fast, fluid self-service data discovery, visualization, and storytelling.
- Self-service machine learning capabilities that identify patterns, clusters, outliners, and anomalies in any data.
Accelerate Time to Action
Autonomous Data Warehouse
Oracle offers Autonomous Data Warehouse that helps operate a data warehouse and secure data using machine learning to self-tune and automatically optimizes performance during the database run time. Built on next-gen database technology and artificial intelligence, it delivers unprecedented reliability, performance and highly elastic data management that enable data warehouse deployment in seconds.
Reduce Cost and Risk
Oracle ADW leverages migration benefits to customers like cost-effectiveness on migration by up to 50%.
It provides an easy-to-use platform for analysts, to:
- Run SQL queries on their data lake
- Create multiple visualization
- Explore query results from different perspectives
- Build and share dashboards
Databricks Data Science & Engineering
It provides an interactive workspace that enables collaboration between data engineers, data scientists, and machine learning engineers.
Databricks Machine Learning:
It's an integrated end-to-end machine learning environment, incorporating:
- Managed services for experiment tracking
- Model training
- Feature development and management
- Feature and model serving
- Innovate Faster: With Databricks on Google Cloud, you can build open, flexible data lakes that are integrated with Google data products like BigQuery and Looker.
- Enable Efficiency for your Analytics: Google Cloud's infrastructure delivers a fast, standardized, scalable Databricks experience.
- Simplify Data Analytics Infrastructure: Databricks leverages Google Kubernetes Engine, Google Cloud IAM, and Google Identity to deliver a scalable and secure experience.
Enhance your Databricks Experience with Google Cloud Open Platform:
- Databricks delivers a fully managed Spark experience on Google Cloud with performance gains of up to 50x over open-source Spark.
- With Databricks Workspace, we can access data from BigQuery to build models and visualize with Looker.
- Databricks on Google Cloud leverages Google Cloud's secure, managed Kubernetes service, Google Kubernetes Engine (GKE), to support containerized deployments of Databricks in the cloud.
Why Databricks on AWS?
- Simple:It enables very simple, unified data architecture on S3 for SQL analytics, data science and machine learning.
- Better Price Performance: SQL-optimized compute clusters provide data warehouse performance at data lake economics.
- Proven and Trusted Performance: Databricks on AWS provides a game-changing analytics platform that addresses all analytics and AI use cases.
Integration with all the popular Data and AI services:
- EC2 instances
- Kinesis streams
- S3 buckets
- IAM instance, and other services.
SnowFlake offers support for an Azure-based data warehousing, including:
- Integration and easy access from Azure Blob Storage to Snowflake
- Easy implementation of data pipelines from Azure Data Lake into Snowflake
- Connection with Microsoft PowerBI Desktop to visualize analytics
Why rely on Snowflake on Azure for your enterprise data?
Secure sharing and collaboration of data:
It enables seamless data management by eliminating the need for data movement in specific cases such as monetization or for your partners.
Multi-clustered shared architecture:
It enables you to perform data reconciliation and management while accessing the same copy of data.
Low maintenance cloud data platform:
Snowflake lets you choose any combination of infrastructure providers, which helps you access and manage your workloads wherever you want.
Snowflake: An AWS Partner for the Cloud Data Warehouse
As an Amazon Web Services partner, Snowflake offers a full range of support for AWS-supported data warehousing, including:
Support for AWS PrivateLink:
It enables Snowflake customers to connect to their Snowflake instance quickly and securely without having to use the public Internet.
It gives Snowflake customers an automated, cost-effective service to load data from AWS into Snowflake.
Easy integration with AWS Glue:
To flexibly manage data transformation and ingestion pipelines.
Snowflake data access for AWS Sagemaker:
To simplify data preparation times and establish a single source of truth for Amazon's new machine learning modelling services.
How Snowflake can be configured to allow high-performance Data loading from Google Cloud Storage (GCS)?
- Create Google Cloud Storage (GCS) Integration
- Grant Permissions
- Setting Up the Snowflake Environment
- Loading the Data
- BigQuery ML enables data scientists and data analysts to build and operationalize ML models on planet-scale structured or semi-structured data.
- BigQuery Omni is a flexible, fully managed, multicloud analytics solution that allows you to cost-effectively and securely analyze data across clouds such as AWS and Azure.
- BigQuery BI Engine is a BigQuery-built in-memory analysis service that allows users to interactively analyse large and complex datasets with sub-second query response time and high concurrency.
BigQuery Omni accesses Amazon S3 data through connections. Each connection has its own unique Amazon Web Services (AWS) Identity and Access Management (IAM) user.
Creating an AWS IAM policy for BigQuery
Ensure that you follow security best practices for Amazon S3. We recommend that you do the following:
- Set up an AWS policy that prevents access to your S3 bucket through HTTP.
- Set up an AWS policy that prevents public access to your S3 bucket.
- Use S3 server-side encryption.
- Limit permissions granted to the Google Account to the required minimum.
- Set up CloudTrails and enable S3 data events.
- Create a connection: This step authorizes BigQuery Omni to read the data in your Azure storage.
- Create an external table: Create a BigQuery external table that references the raw data in Azure storage. The data can be in Avro, CSV, JSON, ORC, or Parquet format. As with other BigQuery tables, BigQuery can infer the table schema. You can also manually specify a schema for CSV or JSON data.
- Run queries: Once the external table is created, you can use Standard SQL to query the data, like any other BigQuery table. For more information, see Overview of querying BigQuery data.
- Export query results to Azure Storage. Optionally, you can write query results to your Azure storage, in which case there is no cross-region copy of the results data.
Accelerate your time to insights with cloud data warehousing at scale that is quick, simple, and secure.
It helps to:
- Concentrate on extracting insights from data in seconds with simple analytics for everyone. Do not even consider managing your data warehouse infrastructure.
- Analyze all of your data sources, including operational databases, data lakes, data warehouses, and third-party data sets.
- Improve query speed, you can achieve up to three times better price performance than other cloud data warehouses at scale.
- Improve financial and demand forecasts
- Collaborate and share data
- Optimize your business intelligence
- Increase developer productivity
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