Data Lake (unstructured and structured data) + Analytics (upto 5 KPI's)

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?

Data Analytics helps grow businesses by providing critical insights and by provisioning cost-effective data driven decision making. It also assists in driving lead in sales, remove impediments from business processes, amends marketing and business spend, manage cost, and enhance client custody. Data analytics provides valuable, actionable insights into ways to grow business that measures business performance and customer base.

Functional Adaptability

Data analytic enables companies to identify potential opportunities that help find hidden bugs and streamlines operations to eliminate them and maximize profit.

Product Development

Data analytics delivers a combo of know-how-forecast capability that helps keep tracking business and give a solid brick to future outcomes.

Conversion Rate Optimization

Digital analytics boosts CRO that enables the business to create online visitor traffic and enhances the current business scenario.

Customer-oriented content

Data analytics effectively enhances a customer-based content that enables companies to personalize their services targeting a circle of customers.

Google Analytics

G-Suite increases your business revenue and improving key performance metrics, offering a ready-go strategy for capturing and mitigating business challenges.

Your Vision, Our Expertise

Elevating Your Software Product Engineering Journey with Vast Edge

Insight-driven ads
Google Analytics is an extremely powerful tool that delivers a deeper understanding to customers, reports and tracks website traffic.

1
Powerful business insight
2
Integration with Google ads
3
Configuration with Google search, YT & partner apps
4
Promote purchases across websites

Optimize bids
Google Analytics streamlines a campaign performance that helps track their customers, and their clicks to purchase or lead capture and increase bids for high performing keywords in Google ads.

1
Advanced customer identified.
2
Adjust cost-per-click.
3
High conversion rate and traffic.
4
Implements Bid Simulator on the first page.
5
Page recommendations for bid performance.

Advanced Machine Learning
Google analytics uses machine learning that enables customers to get out of their data, deliver fasten surface insights and decision making in an informed way.

1
Quicker answers to customers.
2
Deploys a smart list to reach valuable customer.
3
Deliver a variety of signals location.
4
Creates an audience list of visitors.
5
Dynamically 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.

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.

1
Fasten data literacy
2
BI and Analytics support
3
Data-driven objective
4
Examining and confirming user needs

Hassle-free access
Looker helps companies getting easy access to their sensitive real-time data that delivers fresh and very accurate results giving better reporting.

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Real-time dashboard
2
In-depth data analysis
3
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.

1
Next-level BI security .
2
BI governance infrastructure.
3
keeps manage data growing needs.
4
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.

1
Guidance for executive.
2
Helps companies in growth.
3
Off-culture management strategy.
4
Promotion of data-sharing practices.
5
Increased availability of training in data analytics.

Business Gains
It provides businesses with an edge over their rivals and makes advanced business decisions.

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Un-tackled traditional data.
2
Open-source software framework.
3
Integrated with big data analytics.
4
Evaluate volumes of transactional data.
5
Support Hadoop, MapReduce and NoSQL databases.
6
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.

1
Focus on customer-centric objectives.
2
Helps companies understand customers better.
3
Uses all accessible internal sources of data.
4
Develop meaningful relationships with customers.
5
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
Offered analytics experience that makes it faster and easier for you to consume, socialize, and share contextual insights.

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Scheduled delivery of pixel-perfect reports.
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Enable easy capturing of data from the world.
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Freely available Oracle Data Visualization content packs.
4
Personalized and proactive insights keep you updated in real-time.
5
Enables quick-start self-service analytics for key roles in SaaS apps.
6
Collaboration tools empower you to socialize insights and drive results.
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Conversational interfaces enable you to use voice and search to ask questions.
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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.

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Easy self-service data preparation and blending.
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Ad hoc analysis and reporting that is fully mobile.
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Automatic visualization of insights and one-click advanced analytics.
4
Adaptive user experience to adjust the display depending on the device.
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Fast, fluid self-service data discovery, visualization, and storytelling.
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Self-service machine learning capabilities that identify patterns, clusters, outliners, and anomalies in any data.

Accelerate Time to Action
OAC is a complete platform for data-driven innovation that leverages:

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Seamless hybrid deployment options, including lift and shift from on-premises.
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Elastic services that enable you to use and pay for only the resources you need.
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Centralized storage and management for all cloud data, both managed and self-serving.
4
Enterprise-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

Autonomous Data Warehouse offers a single platform that empowers companies to raise questions of any data type. Additionally, the data loading and data analyses let you extract data insights quickly and make real-time critical decisions.

Easy Migration

Oracle using SQL developer helps companies migrate their data warehouse or data lakes to autonomous data warehouse in seconds. Database migration in ADW deploys support for all database providers like Redshift.

Preserve Existing Investment

Oracle delivers 100% autonomous data workloads that help customers leverage their existing skills and investment on ADW.

Reduce Cost And Risk

Oracle ADW leverages migration benefits to customers like cost-effectiveness on migration by up to 50%.

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

Power BI offers predictive power of advanced analytics that allow users to create predictive models from their data enabling organizations to make data-based decisions across all aspects of their business.

>> 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

Grouping data helps users for a clearer view, analyze, and explore data and trends in visuals.

>> 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.

Data Grouping and Binning

R-Integration

BI-R integration helps users generate all stage insights and import resulting data sets into a Power BI data model.

>> 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.

Shares a common relational environment rather than a mix of calculation engines with incompatible data structures and languages.

A day-to-day examination of social media content allocates quantified, timely and attentive results quickly.

Helps minimize learning efforts through the use of careful syntax design. The amount of time required for enhancements, maintenance and upgrades are minimized as well.

Oracle's in-database analytical functions and features enable significantly better query performance by removing the need for specialized data processing.

Fully optimize the internal processing of purpose-built functions and helps companies in generating business intelligence and improve decision making.

Business gets access to a fastened understanding of the current market scenario and the need of brand-new product development.

Improves management by provisioning a consolidated view of all your data.

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

It provides an easy-to-use platform for analysts, to:

>> Run SQL queries on their data lake
>> Create multiple visualizations
>> 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

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:

1
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.
2
Enable Efficiency for your Analytics: Google Cloud's infrastructure delivers a fast, standardized, scalable Databricks experience.
3
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:

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Databricks delivers a fully managed Spark experience on Google Cloud with performance gains of up to 50x over open-source Spark.
2
With Databricks Workspace, we can access data from BigQuery to build models and visualize with Looker.
3
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.

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?

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Simple:Simple: It enables very simple, unified data architecture on S3 for SQL analytics, data science, and machine learning.
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Better Price Performance:Better Price Performance: SQL-optimized compute clusters provide data warehouse performance at data lake economics.
3
Proven 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:

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EC2 instances
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Kinesis streams
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S3 buckets
4
Glue
5
Athena
6
Redshift
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QuickSight
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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:

1
Integration and easy access from Azure Blob Storage to Snowflake
2
Easy implementation of data pipelines from Azure Data Lake into Snowflake
3
Connection with Microsoft PowerBI Desktop to visualize analytics

Why rely on Snowflake on Azure for your enterprise data?

1
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.
2
Multi-clustered shared architecture:

It enables you to perform data reconciliation and management while accessing the same copy of data.
3
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 â€“ 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:

1
Support for AWS PrivateLink:

It enables Snowflake customers to connect to their Snowflake instance quickly and securely without having to use the public Internet.
2
Snowflake Snowpipe:

It gives Snowflake customers an automated, cost-effective service to load data from AWS into Snowflake.
3
Easy integration with AWS Glue:

To flexibly manage data transformation and ingestion pipelines.
4
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.

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)?

1
Create Google Cloud Storage (GCS) Integration
2
Grant Permissions
3
Setting Up the Snowflake Environment
4
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.

1
BigQuery ML enables data scientists and data analysts to build and operationalize ML models on planet-scale structured or semi-structured data.
2
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.
3
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 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

1
Set up an AWS policy that prevents access to your S3 bucket through HTTP.
2
Set up an AWS policy that prevents public access to your S3 bucket.
3
Use S3 server-side encryption.
4
Limit permissions granted to the Google Account to the required minimum.
5
Set up CloudTrails and enable S3 data events.

BIGQUERY OMNI WITH AZURE

1
Create a connection: This step authorizes BigQuery Omni to read the data in your Azure storage.
2
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.
3
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.
4
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:

1
Concentrate on extracting insights from data in seconds with simple analytics for everyone. Do not even consider managing your data warehouse infrastructure.
2
Analyze all of your data sources, including operational databases, data lakes, data warehouses, and third-party data sets.
3
Improve query speed, you can achieve up to three times better price performance than other cloud data warehouses at scale.

Use Cases:

1
Improve financial and demand forecasts
2
Collaborate and share data
3
Optimize your business intelligence
4
Increase developer productivity

FAQs

Frequently Asked Questions

Q1: What is Big Data Analytics?

Big Data Analytics is the process of examining large and varied data sets to uncover hidden patterns, unknown correlations, market trends, customer preferences, and other useful business information.

Q2: How can Big Data Analytics benefit my business?

Big Data Analytics can benefit your business by providing valuable insights that can inform decision-making, improve operational efficiency, enhance customer experiences, drive innovation, and ultimately increase revenue and profitability.

Q3: What is Databricks and how is it used for Big Data Analytics?

Databricks is a Unified Data Analytics platform designed to help data teams solve the world's toughest problems. It simplifies data engineering, data science, and machine learning processes to extract value from data efficiently.

Q4: What is RedShift and how is it used for Big Data Analytics?

Amazon Redshift is a fully managed, petabyte-scale data warehouse service in the cloud. It is designed for high-performance analysis and reporting on large datasets, making it ideal for Big Data Analytics projects.

Q5: What is BigQuery and how is it used for Big Data Analytics?

Google BigQuery is a serverless, highly scalable, and cost-effective multi-cloud data warehouse. It enables users to run fast, SQL-like queries on large datasets, making it a powerful tool for Big Data Analytics.

Q6: What is Snowflake and how is it used for Big Data Analytics?

Snowflake is a cloud-based data warehousing platform that allows users to store and analyze large amounts of data with ease. It delivers performance, simplicity, and flexibility for data analytics processes.

Big Data Analytics | DataBricks, RedShift, BigQuery, SnowFlake