Data Insights
Data Insights helps transform disparate data into actionable insights that keep you aligned with your business objectives.
The Data Insights process is divided into 3 phases:
Phase 1: Discovery
Involves identifying data sources and transformation requirements to select the best architecture for ingest, processing, and analysis.
Duration: ~ 50 hours
Goals & Objectives
- Improving data accessibility
- Enabling self-service analytics
- Accelerating time-to-insights
- Facilitating data-driven decision-making
- Fostering innovation
Design Considerations
- Choosing between a data lake or warehouse
- Data ingest patterns
- Data transformation requirements
- Data access patterns
- Security and compliance requirements
- Integration with existing systems
Deliverables
- Data lake or warehouse architectural design
- Blueprint for data lake or warehouse service delivery
Phase 2: Implementation
Involves the implementation of a Data Lake or Data Warehouse that aligns with specified goals and objectives.
Duration: 2-3 Weeks for Small Implementations, 3+ Weeks for Larger Implementations
Data Lake
- Configuration of Amazon S3 for data storage, TTL, versioning
- Configuration of AWS Glue of data cataloging and ETL
- Integrating AWS Athena for serverless querying
- Adding AWS EMR for big data processing (optional)
- Ingesting and configuring the data lake/warehouse
- Extracting, transforming, and loading data with AWS Glue
- Managing the data catalog and metadata
- Setting up data governance and access control per best practices
Data Warehouse
- Configuration of Amazon Redshift for data storage and querying
- Configuration of data structure, tables, and schema
- Ingesting the data in the data warehouse
- Setting up data governance and access control per best practices
- Analyzing and visualizing data
Phase 3: Delivery
Our commitment to your success extends beyond the initial implementation. Data Insights includes a monthly check-in where we review data and insights, fine-tune data sources, and identify any areas of improvement.
Data Insights can also be combined with CloudWise, our Managed Services offering, to ensure the optimization of your entire AWS infrastructure.
Factors that influence cost
Number of data sources
Amount of data ingested
Number of required users (author and reader)
Number of dashboards and visualizations
Frequency of data refresh
ARCHITECTURE EXAMPLE
