Big Data Analytics | AI Data Analysis | DataBricks, RedShift, BigQuery, SnowFlake
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 for business?
Data analytics can help grow businesses by providing needed insights and hence enabling cost-effective, data-driven decision-making. Using advanced data analytic tools, AI data analysis, and a secure platform, organizations can get sales leads, remove impediments from business processes, optimize marketing and business spends, manage costs, and have a loyal customer base. Companies also gain actionable insights that measure business performance, improve operational efficiency, and expand their customer base.
Product Development
Conversion Rate Optimization
Customer-oriented content
Google Analytics
G-Suite increases your business revenue and improving key performance metrics, offering a ready-go strategy for capturing and mitigating business challenges.
Insight-driven ads
- Powerful business insight
- Integration with Google ads
- Configuration with Google search, YT & partner apps
- Promote purchases across websites
Optimize bids
- 01Advanced customer identified.
- 02Adjust cost-per-click.
- 03High conversion rate and traffic.
- 04Implements Bid Simulator on the first page.
- 05Page recommendations for bid performance.
Advanced Machine Learning
- 01Quicker answers to customers.
- 02Deploys a smart list to reach valuable customer.
- 03Deliver a variety of signals location.
- 04Creates an audience list of visitors.
- 05Dynamically manage audience list.
Looker Analytics
Looker Analytics is an integrated business intelligence software and big data analytics platform that delivers data-driven insights and allows exploration, analysis, and sharing real-time business analytics.
Hassle-free access
- Real-time dashboard
- In-depth data analysis
- Single point of data access
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
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 offers companies with strategies that help manage and analyse volumes of data while delivering real-time valuable insight from platforms like social networks, videos, digital images, sensors, and sales transaction records.
Fact-based decision-making
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.
Business Gains
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
- →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
Oracle offers data processes like discovering, interpretation and communication and enterprise-grade tooling that empower companies to raise questions from any device.
Extend Insight Consumption
- 01Scheduled delivery of pixel-perfect reports.
- 02Enable easy capturing of data from the world.
- 03Freely available Oracle Data Visualization content packs.
- 04Personalized and proactive insights keep you updated in real-time.
- 05Enables quick-start self-service analytics for key roles in SaaS apps.
- 06Collaboration tools empower you to socialize insights and drive results.
- 07Conversational interfaces enable you to use voice and search to ask questions.
- 08Autonomous analytics generating natural language processing of attributes and virtual reality experiences.
Power Deeper Insights
- 01Easy self-service data preparation and blending.
- 02Ad hoc analysis and reporting that is fully mobile.
- 03Automatic visualization of insights and one-click advanced analytics.
- 04Adaptive user experience to adjust the display depending on the device.
- 05Fast, fluid self-service data discovery, visualization, and storytelling.
- 06Self-service machine learning capabilities that identify patterns, clusters, outliners, and anomalies in any data.
Accelerate Time to Action
- 01Seamless hybrid deployment options, including lift and shift from on-premises.
- 02Elastic services that enable you to use and pay for only the resources you need.
- 03Centralized storage and management for all cloud data, both managed and self-serving.
- 04Enterprise-grade granular security via a combination of roles, entitlements, and object and data-level protection.
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.
Complete Analytical Solution
Easy Migration
Preserve Existing Investment
Reduce Cost And Risk
Microsoft Power BI
Microsoft Power BI offers business analytics that helps users create their reports and dashboards for extracting valuable knowledge from data to solve business problems and deliver deeper data insight.
Predictive Analytics with Azure
➥ Grow and change with advanced data
➥ Leverages speech recognition programs
➥ Effective web searches
➥ Create predictive models quickly
➥ Drag, drop, and connect data modules.
Data Grouping and Binning
➥ Manually aggregates data points into groups.
➥ Bin the results by setting the size of each bin.
➥ Automatically patching to new or refreshed data.
➥ Apply patching to date fields and numeric fields.
R-Integration
➥ R visuals in Power BI.
➥ Advanced analytics depth
➥ Power Query performs advanced data cleansing.
➥ Outlier detection and missing values completion.
➥ Visualize data by gaining endless flexibility.
SQL Data Warehouse
SQL analytics offers a system to analyze data with particular statistics by providing a mature and comprehensive framework for data access. With enhanced developer productivity, it simplifies the application code by replacing complex analytical processing.
Databricks: Making Data Simple
Databricks is focused on making big data simple so that every organization can turn its data into value. Big data means to improve businesses, save lives, and advance science by analyzing and processing data. Databricks is built around Apache Spark and consists of two additional components: a hosted platform (Databricks Platform) and a workspace (Databricks Workspace). Databricks Platform is a hosted platform that makes creating and managing clusters a breeze. Databricks Platform includes a sophisticated cluster manager that enables users to have a cluster up and running in seconds while providing everything they require out of the box.
Databricks on Azure
Azure Databricks offers three environments for developing data-intensive applications: Databricks SQL, Databricks Data Science & Engineering, and Databricks Machine Learning.
Databricks SQL
➥ Run SQL queries on their data lake
➥ Create multiple visualizations
➥ Explore query results from different perspectives
➥ Build and share dashboards
Databricks Data Science & Engineering
Databricks Machine Learning:
➥ Managed services for experiment tracking
➥ Model training
➥ Feature development and management
➥ Feature and model serving
Databricks on Google
The open lakehouse platform from Databricks is fully integrated with Google Cloud's data services, allowing you to consolidate your analytics applications onto a single open cloud platform. It also lets you store all of your data on a simple, open lakehouse platform that combines the best of data warehouses and data lakes to unify all of your analytics and AI workloads.
Benefits:
- 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:
- 01Databricks delivers a fully managed Spark experience on Google Cloud with performance gains of up to 50x over open-source Spark.
- 02With Databricks Workspace, we can access data from BigQuery to build models and visualize with Looker.
- 03Databricks on Google Cloud leverages Google Cloud's secure, managed Kubernetes service, Google Kubernetes Engine (GKE), to support containerized deployments of Databricks in the cloud.
Databricks on AWS
It provides a collaborative workspace for data science, machine learning, and analytics. Databricks on AWS enables you to store and manage all of your data on a straightforward, open lakehouse platform that combines the best of data warehouses and data lakes to unify all of your analytics and AI workloads.
Why Databricks on AWS?
- 01Simple:
Simple: It enables very simple, unified data architecture on S3 for SQL analytics, data science, and machine learning. - 02Better Price Performance:
Better Price Performance: SQL-optimized compute clusters provide data warehouse performance at data lake economics. - 03Proven and Trusted Performance:
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
- Glue
- Athena
- Redshift
- QuickSight
- IAM instance, and other services.
Snowflake
Snowflake's simple cloud data platform, which includes a data warehouse as a service (DWaaS) and a cloud data lake, offers a cloud-based single solution to Big Data management requirements. The Snowflake Data Cloud supports modern data and data application workloads. Snowflake provides the platform that data scientists can rely on for analytical initiatives.
With Snowflake, businesses are tearing down data silos so that their teams can spend less time managing infrastructure and more time turning data into insights.
Snowflake on Azure: Data Warehouse
To facilitate secure data storage, real-time analytics, and concurrent, enterprise-wide data sharing without exorbitant capital expenditures, businesses are turning to Cloud-based data services such as Microsoft Azure and SaaS data warehousing.
To store data, extract valuable insights, and share these insights in real time, the ideal Azure data warehouse must seamlessly combine the power of Cloud computing services with the flexibility, access, and analytics power of SaaS data warehousing.
SnowFlake offers support for 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 on AWS:
Snowflake on AWS combines this potent combination with a SaaS-built SQL data warehouse that manages disparate data sets in a single, native system. Snowflake scales workload, data, and user demand automatically to provide full elasticity businesses only pay for what they require.
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:
- 01Support for AWS PrivateLink:
It enables Snowflake customers to connect to their Snowflake instance quickly and securely without having to use the public Internet. - 02Snowflake Snowpipe:
It gives Snowflake customers an automated, cost-effective service to load data from AWS into Snowflake. - 03Easy integration with AWS Glue:
To flexibly manage data transformation and ingestion pipelines. - 04Snowflake 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.
Snowflake on GCP
Customers can now use Snowflake alongside Google Cloud's comprehensive set of advanced analytics and machine learning solutions to derive meaningful insights from various data sources. Snowflake customers will be able to seamlessly and securely store data in Google Cloud Platform, gaining access to the performance, scalability, and security of Google Cloud alongside their preferred analytics warehouse.
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
Google Bigquery: Cloud Data Warehouse
It is a serverless, highly scalable, and cost-effective multi-cloud data warehouse designed for business agility. It democratizes insights with a secure and scalable platform with built-in machine learning. Power business decisions from data across clouds with a flexible, multi-cloud analytics solution.
- 1BigQuery ML enables data scientists and data analysts to build and operationalize ML models on planet-scale structured or semi-structured data.
- 2BigQuery 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.
- 3BigQuery 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 AWS Connection
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
- →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.
Bigquery Omni with Azure
- 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.
Amazon Redshift: Cloud Data Warehouse
Accelerate your time to insights with cloud data warehousing at a scale that is quick, simple, and secure.
It helps to:
- 01Concentrate on extracting insights from data in seconds with simple analytics for everyone. Do not even consider managing your data warehouse infrastructure.
- 02Analyze all of your data sources, including operational databases, data lakes, data warehouses, and third-party data sets.
- 03Improve query speed, you can achieve up to three times better price performance than other cloud data warehouses at scale.
Use Cases:
- →Improve financial and demand forecasts
- →Collaborate and share data
- →Optimize your business intelligence
- →Increase developer productivity




