Data, AI & Analytics

SNP Technologies will deliver the following consulting services:

SNP Offers:

• Data & Insights Maturity Assessment
• “Right Fit” Architecture Recommendation
• Data Modernization: Data modernization is one of the most trusted and reliable ways to achieve an AI-driven foundation with a tested method and platform that sources all the critical data and delivers enterprise-wide intelligence. Organizations can transform data into an asset with data modernization. The technology and framework delivered by this are required to re-invent, re-engineer and augment data platforms with real-time analytics.

Business leaders can leverage data as an asset and scale up their productivity with data modernization by:

• Data Of All Types Are Available For The Business: Businesses can refine data quality and integrate different data types as they develop their big data analytics to include social media, IoT, and third-party data within a framework of ethical governance.
• Creating Actionable Insights: Teams are empowered to leverage data whenever they need to understand what, why and how to get the best possible outcomes.
• Driving Performance: Agility and competitiveness become the core to deliver reduced time to market, support the development of new products and services, streamline operations and improve customer services.

SNP Delivers Data Modernization Using Azure Data Platforms

• Azure SQL Database: The classic lift and shift managed instance that helps organizations save up to 59% vs AWS. It is intelligent, fully-managed relational cloud database services with built-in security.
• SQL Server on Azure VMs: A SQL server installed and hosted in the cloud which provides full control over the database engine. It comes along with auto-patching and auto-backup which helps you save up to 43% vs AWS.
• Azure Database for MySQL, PostgreSQL, and MariaDB: Managed services using language and framework of your choice with built-in high availability and dynamic scaling. Businesses can save up to 50% on HA vs AWS.

SNP Technologies and Microsoft bring the power and ease of using Azure Databricks Platform-as-a-Service (PaaS) to modernize your data warehouse with a robust Apache SparkTM analytics platform in the cloud for high performance and global scalability. Azure Databricks provides a fast, easy and collaborative analytics platform to accelerate and simplify the process of developing dig data and artificial intelligence solutions backed by industry leading SLAs.

Advantages of Azure Databricks

• Increase in productivity and collaboration by bringing teams together in a collaborative workspace. From data gathering to model creation, teams can use Databricks to amalgamate the process and instantly deploy to production.
• Develop a secure and trusted cloud by protecting your data and business with Azure Active Directory integration, role-based control and enterprise grade SLAs. With fine-tuned user permissions, secure access to Databricks, clusters, workflows and data, organizations can attain peace of mind.
• Global scaling of analytics and data sciences projects. Organizations can build and innovate faster using machine learning capabilities. Reduce cost and complexity with a fully managed cloud native platform.
• Azure Databricks supports Python, Scala, R, Java and SQL as well as data science frameworks. It provides interactive workspace for the developers to collaborate with popular tools, languages and frameworks. Your business can access high performing data warehousing for unparalleled levels of scalability and performance in conjunction with Azure Synapse Analytics.
• SNP’s Databricks solution covers a broad range of use cases including core ETL, data discovery and exploration, data warehousing, data product deployment, and insight publishing using dashboards. Our solution can integrate large volumes of data with ease for the purpose of data preparation, integration as well as for analytics purpose like Machine Learning (ML) models. By leveraging Azure Databricks, we help our customers integrate data from various sources like on-premise, cloud, data warehouse, data marts, and data lakes etc.

SNP Technologies’ Azure database migration services are 360o of managed services designed to perform migrations from multiple database sources to the Azure cloud platform with negligible downtime. It provides businesses with a highly available and comprehensive solution. SNP would integrate some of the existing Azure tools and services in the data migration services.

SNP’s Data Migration Process

• Pre-migration Services: The pre-migration process starts with the discovery process where the database assets and application stack are recorded. The next step is the assessment of workloads to fix recommendations. The last step in the pre-migration services is converting the source schema to work in the targeted environment.
• Migration: This is the step where the actual migration takes place in which the source data, source schema, and objects are moved to target. After migration, the target schema is synced with the data and the source. The last step is the cutover from the source to the target environment.
• Post-migration Services: SNP will make necessary changes to the applications in a predefined iterative manner and perform functional and performance tests. According to the test results, we will address the performance issues and retest to confirm the performance improvements.

Benefits Of Moving To Cloud With Azure SQL Database

• Productivity & Performance Hype: Organizations get up to 100 TB of on-demand scalable storage per DB.
• Unbeatable Security: Organizations are ensured of 99.99% availability of SLA and layers of security.
• Built-in intelligence: Businesses can reap the benefits of intelligent performance tuning and intelligent protection
• Seamless Compatibility: The migration provides the broadest SQL server which is highly compatible with VNET support.
• Competitive TCO: Organizations can save up to 80% of overhead costs with Azure hybrid benefit and reserved capacity.

A modern data warehouse takes modern and complex data processing up a notch. It brings together data of an organization of any scale easily and provides insights through analytical dashboards, reports and advanced analytics for all the users across the organization.

What A Data Warehouse Delivers For Your Organization

• You can combine all your structured, unstructured and semi-structured data using Azure Data Factory to Azure Blob Storage
• Organizations can now leverage data in Azure Blob Storage and perform scalable analytics with Azure Databricks to achieve data transformation.
• Once the data is transformed, it can be moved to Azure Synapse Analytics to combine existing structured data, thus, creating a hub for all the organizations’ data. Data is scaled by leveraging connectors between Azure Databricks and Azure Synapse Analytics.
• Organizations can develop reports and analytical dashboards on top of Azure Data Warehouse to derive insights from data.
• Databricks allows users to run ad hoc queries on data stored within.

How Data Warehouse Works

• Ingest: Azure Data Factory allows code-free data ingestion from 75+ data integration connectors.
• Store: Azure Data Lake provides a highly scalable solution for big data analytics with a T-SQL query over any data with 99.9% SLA.
• Prep: It provides a fast, easy and collaborative Apache Spark-based analytics platform. With Azure Databricks, organizations can go up to 10x faster than vanilla Spark.
• Serve: A SQL based, fully managed, petabyte-scale cloud data warehouse stores and processes all the data. Azure SQL Data Warehouse is 10x faster than Redshift.
• Reporting: Power BI aids in generating powerful reports once the data is processed and refined.

Cloud has changed the way applications are designed and data is processed and stored. Long gone are the days of a one-stop solution single database that handles all of the solution’s data. Technology now allows the users to use multiple, specialized storage, each optimized to serve specific capabilities. Multiple layers of business logic that are read and written in a single data layer is obsolete. Solutions are now designed and developed around the data pipeline that describes how data flows through a solution, where it is processed, where it is stored and how it is consumed.

Data Is Structured In Two Ways

• Traditional RDBMS Workloads: The workloads that include Online Transaction Processing (OLTP) and Online Analytical Processing (OLAP) are classified here. OLTP systems process typically relational data with a predefined schema and a set of constraints. Multiple data sources emerging for the organization are consolidated into a data warehouse to move and process the source data.
• Big Data Solutions: Traditional databases cannot handle large and complex data and therefore big data is the way to handle them. Big data architecture is designed to handle the ingestion, processing, and analysis of large and complex data. The data here is processed in real-time and it involves a large amount of non-relational data.