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Big Data Analytics Solutions

Any business transformation initiative is only as successful as the foundation it is built on.

With Data Engineering accounting for 80% of the effort in most transformational initiatives, modernized solutions that accelerate the process are key to optimizing your investment and reaching your unique goals. 
DataFactZ does things quicker. With cross-discipline Data Engineering expertise – from implementation to oversight – your organization can reap benefits in weeks, not months, and stay ahead of tomorrow.

We empower efficiency through various large-scale data warehousing initiatives with minimal risk, lending to a proven methodology for immediate and future success.

Data Architecture

We design conceptual, logical, and physical data models, while reengineering existing ones to meet your ever-changing business demands.

Data Analysis

Our proven methodology identifies the right data sources for integration, ensuring that the data profiling and quality you receive is accurate, consistent, and standardized.

ETL Development

With Industry-leading ETL tools such as Informatica, IBM DataStage, Pentaho and Talend, we have the expertise required to handle robust warehousing implementations with stability, efficiency and timeliness.

As the amount of data continues to skyrocket, organizations need a tailored combination of tools and technologies to stay ahead of the curve.

Evolved towards industry best practices, DataFactZ holds the expertise to help diverse organizations navigate their big data initiatives. Our overall strategy guides organizations through an interdisciplinary approach, complemented by our advanced analytic solutions that maximize ROI.

Our Approach

Migrating data from on-premise data warehouses to the cloud allows organizations to achieve scalability, flexibility, and performance at a lower cost.

At DataFactZ, we understand both the technical and operational challenges in cloud migrations. By preparing a tailored roadmap for cloud migration, our techno-functional experts are saving enterprises from costly migration failures.

Our cloud migration approaches majorly fall into these 3 categories:

Lift & Shift

Move data warehouse as-is to the Cloud

Replica on Cloud

Replicate complete or partial data in the Cloud

New DWH

Build a new data warehouse from scratch on the Cloud

Migrating from Teradata to Snowflake?

Planning
  • Business use case approval
  • Scope definition
  • Success criteria
  • Final State Architecture
  • GAP analysis
  • Migration Plan
  • Training plan
Code Refactoring
  • Data model conversion
  • Teradata SQL to Snowflake SQL
  • Bteq Files to Python
  • Stored Procedures to JavaScript
  • Performance optimization
  • Warehouse setup
  • Security implementation
Historial Data Migration
  • Data Loading Strategy
  • Data Transfer using DataFactZ’s custom Accelerators
  • Data Unloading Strategy by using Snowflake Virtual DWs
Incremental Data
  • Databricks code refactoring for incremental loads
  • Design and build pipelines for Snowflake integration
  • Build transformations using Snowflake SP
  • Performance optimization
  • SLA attainment
Data Validation
  • Data accuracy testing
  • Performance testing
  • Power BI Databricks and other consumption testing
  • Parallel testing
  • Security validation
Go Live
  • End user access and usage
  • Success criteria validation
  • Monitoring
  • Business support
  • Training

Diversified platforms can cause growing amounts of information to be scattered.

The DatafactZ advantage optimizes Data Governance while providing the framework for the continuous improvement of Data Quality and Integration. The result? Having greater insights and ever greater value.

Collection

Loading data into a cluster for analysis from multiple sources and several forms poses an efficiency challenge. Big data prevails through quicker data analysis and the power to analyze the data. Our extensive experience with data loading projects allows us to:
  • Identify required data points
  • Identify data sources and the right drivers
  • Establish data source connectivity
  • Configure and set up the right tools (like Sqoop, Flume, Kafka, and Storm) to use identified data sources and develop scripts for the automated data loading tools
  • Manage workflow for the data loads
  • Schedule and monitoring data load jobs
  • Perform data quality checks

Computation

As big data technologies evolve, it is necessary to use real-time query processing and in-stream processing for multiple business applications. We developed a hybrid architecture that can effectively process data in batch mode and in real-time.
This phase involves the implementation and development of algorithms and scripts. Our team of certified developers are proficient in various programming languages and have success not only in developing these programs and scripts but also in performance enhancement. We monitor jobs and collect statistics to consistently gain insight into potential areas of improvement.
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Consumption

In this final step, valuable information is extracted from massive volumes of data. While there are many types of data analytics, not all show the same picture. DataFactZ has implemented cutting-edge technologies in the following areas: