Data Services
DATA
ENGINEERING
Put enterprise data to work for the business
Rappino provides end-to-end Data Lifecycle Management tailored to evolving data consumption patterns—ensuring consistent accessibility, quality, and governance at every stage.
The Challenge
Businesses need an
efficient and agile
way to source and
consume data
Enterprises today have access to enormous amounts of data from multi-cloud infrastructures. However, their ability to put that data to work is limited due to increasing complexity, poor data management and ill-equipped infrastructure and tools. Overcoming these obstacles requires several things:
- Being able to turn a fast-growing pool of enterprise data into actionable intelligence.
- A trustworthy data foundation and enabling analytics supported by insights from a wide range of data sources.
- Proper data preparation that allows insights from raw data — for all types of analytics. Insights that are available in context-specific patterns for interactive visualizations, and predictive and prescriptive analytics.
What we do
WE OFFER DATA ENGINEERING
SERVICES TO ACCELERATE
DIGITAL EVOLUTION
Our Offerings
List of our Data Engineering Services
- DATA COLLECTION & SUMMARIZATION
Extraction of structured, unstructured data coming from streaming and batch sources and refining/cleansing data to make it available on legacy database or cloud systems, to data scientists and business users for exploration and analysis.
- Data Storage & ELT / ETL
Extracting, processing, transforming, and loading data techniques into various relational, non-relational, NoSQL, big data systems and/or cloud storages, depending on data availability, volume, velocity, and type of data.
- DATA COLLECTION & SUMMARIZATION
An efficient and smart approach for migrating business data to / from on-prem legacy systems into cloud storage infrastructure or new target platforms.
- Data Pipelines
Building production-grade repeatable and independent data workflow pipelines to move, transform and store data.
Using various legacy, Big Data and / or cloud orchestration and data management pipeline tools and techniques like DF, Databricks, Synapse, Informatica, and others, to process data in batch and real time.
- Continuous Integration & Deployment
Expertise in legacy and cloud-based deployment services for developing efficient production build and release pipelines based on infrastructure-as-code artifacts, reference / application data, database objects (schema definitions, functions, stored procedures, etc.), data pipeline definitions and data validation and transformation logics.
- DATA COLLECTION & SUMMARIZATION
Expertise in providing data insights and intelligence on stewardship, compliance and regulatory drivers for client consideration and decision making.
- Data Quality
Automated data quality solutions for critical tasks, including correction, enrichment, standardization, and de-duplication.
THE OUTCOMES WE DELIVER
Monetize and
maximize the value
of data
Benifits of our Data Engineering Services
Faster time-to-value
With accelerators, frameworks, and proven services without compromising quality
Improved Operational Efficiency
Leveraging operational data to improve efficiency; developing ML/AI use cases to improve sales and operations; access distributor data to get better supply chain visibility, identify gaps, and improve replenishment rates
New Customer & Market Insights
Access retail data and integrate market research to gain new insights into consumer behavior and inventory levels
New Customer & Market Insights
Access retail data and integrate market research to gain new insights into consumer behavior and inventory levels
Enhanced Compliance
Integrate regulatory and market data to align sales and distribution
Increased Sales
Leverage online/e-commerce data to fuel sales initiatives
Our methodology
how we do it
how we do it
Our process
Rappino uses a consultative approach that combines data engineering, cloud, data privacy, and compliance expertise with proprietary frameworks and maturity models to construct a modern data ecosystem. In addition, our flexible resourcing model allows for the rapid scaling of teams through a pod or virtual pod-based approach.
DATA STRATEGY
Data strategy and roadmaps in <12 weeks
Delivered using structured methodology
Key Deliverables: Architecture options, business case, ROI driven program schedule, design patterns
DATA MANAGEMENT
Open source, micro-service data services
Comprehensive metadata models
Data catalog, lineage and data virtualization services
Seamless adoption to API and all data consumption patterns
MANAGED DATA SERVICES
iC4TM delivers true self-service capabilities for curation, catalog, context and consumption needs
Growing inventory of analtics as service products – virtual combo, merchant services
TYPICAL ENGAGEMENT
1 16 week assessment w/POC
2 1-2 Year exucution and delivery
3 Ongoing managed data services
Flexible terms
Fixed bid, time and materials, and managed data services
of our Data Services to accelerate the data transformation journey. These include:
M4 Data Strategy Roadmap
A proprietary execution framework for analytics engagements. M4 provides a proven strategy
and predictable steps for data modernization. It helps map use cases to modernize
architecture and migrate to the cloud while giving all stakeholders a clear view of expectations.
M1
Document
‘Business Use Cases’
(M1.1)
Map IT Goals and Business Use Cases (M1.2)
Gap Analysis – Information Consumption (M1.3)
Build Data Roadmap (Core Milestones, Schedule) (M1.4)
BUS Matrix (M1.5)
Map information Governance with Roadmap (M1.6)
Regulatory and Compliance Framework (M1.7)
AI, Machine Learning, Natural Language (M1.8)
M2
Reference Architecture / Data Lake (M2.1)
Enterprise Data Model Design (M2.2)
Data Services Framework / Common Components (M2.3)
Governed Data Pipeline (2.4)
Data Products (DQ, Lineage, Catalog, Discover, Virtualization) (M2.5)
Intergration of Data Engineering and Dta Ops (M2.6)
M3
Cloud Migration – Phase 1 (M3.1)
Cloud Migration – Phase 2 (M3.2)
User Migration (M3.3)
Optimize visualization, transformation, and enrichment (M3.4)
Data Governance Tools (M3.5)
Metadata / Data Lineage Migration (M3.6)
Security & Compliance (M3.7)
M4
Deliver Business Value (M4.1)
Machine Learning / AI, Digital and RPA Integration (M4.2)
Faster Insights (M4.3)
Self-Service Platform (M4.4)
Analytics As a Service (M4.5)
Logical Data Warehouse (M4.6)
m1
map
Map current state challenges with strategic goals to showcase critical milestones (Quarterly view)
Map milestones (Quarterly) with specific data assets, data products and capabilities delivered
m2
modernize
Modernize data architecture and deliver data models for optimized semantic layer, integrated key management process, data pipeline
Modernize information consumption capabilities
m3
migrate
m4
monitize
Data Dip
Our expertise
DATA ENGINEERING AND ANALYTICS TOOLS
Rappino has experience with the leading cloud and analytics tools, and platforms. We take an agnostic and unbiased approach with the goal of selecting the right tools for the organization and environment. We can help you take full advantage of these tools and platforms to maximize your ROI with them.
key
partnerships
why Rappino
EXPERTISE IN DESIGNING & EXECUTING DATA STRATEGY IN DATA-INTENSIVE ENVIRONMENTS
proven
ACCELERATORS FOR
FASTER TIME-TO-VALUE
READY-TO-CONFIGURE DATA
MODELS, FRAMEWORKS, &
MICRO-SERVICES
FAQ’s – Data Strategy
A data strategy is a comprehensive plan that outlines how an organization will collect, manage, analyze, and utilize data to achieve its business goals. It is crucial because it ensures that data is leveraged effectively to drive decision-making, optimize operations, and gain a competitive edge. Without a well-defined data strategy, organizations risk making decisions based on incomplete or inaccurate information, leading to missed opportunities and inefficiencies.
Data strategy services provide businesses with expert guidance and support in developing and implementing a robust data strategy. These services help organizations identify key data sources, define data governance frameworks, and establish a data-driven culture. By leveraging data strategy services, businesses can unlock the full potential of their data, enabling them to make informed decisions, enhance customer experiences, and drive innovation.
A successful data strategy roadmap typically includes several key components: data governance, data architecture, data quality management, data integration, and analytics capabilities. It outlines the steps required to achieve short-term and long-term data objectives, aligning data initiatives with business goals. The roadmap also identifies the tools, technologies, and processes needed to support data-driven decision-making and ensures that data is consistently managed and utilized across the organization.
Developing a data strategy involves several best practices: aligning the strategy with business goals, establishing clear data governance policies, ensuring data quality and integrity, and fostering a data-driven culture. It’s also important to create a flexible data architecture that can scale with your organization’s needs and to invest in analytics tools that provide actionable insights. Regularly reviewing and updating the data strategy roadmap is essential to adapt to evolving business requirements and technological advancements.
A data strategy is a broader, overarching plan that outlines how an organization will use data to achieve its business objectives, while a data management plan focuses on the specific processes and practices for handling data on a day-to-day basis. The data strategy sets the vision and direction for data utilization, including governance, analytics, and innovation, whereas the data management plan details the operational aspects of collecting, storing, and securing data.
