Prepare for uncertainties due to shifting business environments and accelerating technologies that multiply data sources.
Our team of inquisitive data analysts, data engineers and data scientists have guided over 100 clients across multiple industries. Over the decades, we’ve developed a pragmatic and interdisciplinary approach for each initiative, including metadata management, master data management and SOA.
Regardless of your business needs, we will be able to provide valuable solutions that utilize unique technology combinations, methodologies, and tools that support your success.
We focus on building highly scalable and performance-centric solutions for your future needs.
With hundreds of employees spanning five countries, we have managed a multitude of data engineering and analytics projects.
Best-in-class technology partnerships with companies like Microsoft, IBM, Cloudera, MicroStrategy, Talend, Snowflake, and many more.
Optimize the performance of your data.
DataFactZ’s best practices help enterprises build an advanced, highly-scalable data warehouse platform, a robust foundation for business analytics.
Data Models designed with industry standards in mind
Built for high performance on large data sets with optimizations
Made to extend the data model to add data from other domains
Bottom-up approach ensures underlying data warehouse architecture delivers business value
Data Warehouse Services
Sizing, Technology Selection, Design Advisory, conformed dimensions
Build Data Warehouse to get Enterprise-wide Single Version of Truth
Ongoing Maintenance of Data Warehouse
Enhancement & Optimization
Incremental changes in DW design, index optimization
Migration & Upgrade
Transition to platform of your choice
Storage of any type and structure of data
Data Warehouse Modernization
Increase Insight speed while reducing costs. DataFactZ can help you modernize your data Warehouse by migrating to modern cloud-based databases like Synapse, Snowflake and Redshift, always keeping security, flexibility and consistency at top priority.
- Business use case approval
- Scope definition
- Success criteria
- Final State Architecture
- GAP analysis
- Migration Plan
- Training plan
- Data model conversion
- SQL & script conversion
- ETL re-configuration to Snowflake
- Performance optimization
- Warehouse setup
- Security Implementation
Historical Data Migration
- Data Loading Strategy
- Data Transfer
- Data Unloading Strategy
- ETL code refactoring
- ETL implementation
- Performance optimizations
- SLA attainment
- Data accuracy testing
- Performance testing BI, ETL and other consumption testing
- Parallel testing
- Security validation
- End user access and usage
- Success criteria validation
- Business support
DataFactZ is an Snowflake Select Tier Partner
- 5+ Successful Deployments over the Last Year
- Certified Snowflake Specialists
- Retail, Financial Services, Entertainment, Hospitality, Manufacturing etc
- Proprietary Accelerators – Data Pipeline Automation
- Multi-cloud Deployment Expertise
- DataFactZ’s proprietary Snowflake cost and resource monitoring tool that can be deployed on any BI tool
Streamline comprehensive and responsive insights that integrate productivity into multidisciplinary and agile team environments. We have the expertise to create a unified data flow, and implement personalized solutions to enchance process, organization and information distribution.
Many new data warehouse solutions are costly and time-consuming, yet too narrow for your situation to begin with. DataFactZ follows a phased approach with continuous data engineering to ensure your various sources of data never have to compromise their value.
It all starts with an expert assessment of the current ecosystem, followed by a single process roadmap for integrating all your data into a single hub. Our solutions empower you to extract maximum value from the right data while avoiding data silos.
Optimize your business processes through the improved exchange of information
Gain complete visibility into your processes to make more informed decisions
Boost your organizational productivity with holistic access to resources of the organization
Enhance your reporting capabilities by streamlined distribution of the information
- Assessment, Strategy, Product Evaluation, Proof of Concept (PoC), Proof of Technology (PoT)
- Installation & Configuration
- Use Case Implementation
Center of Excellence
- Establishing a Centre of Excellence for the enterprise to establish rules and standards around Data Integration suite of products
- Data management, quality and reporting
Big Data Integration Services
- ETL migration to Big Data Platforms
- Big Data Connectors for integration
- Integrate with Big Data ecosystem like Apache Hadoop, Spark and NoSQL databases
Cloud Integration Services
- Seamless integration of cloud data services with on premise systems
- Rapid deployment of real-time analytics in the cloud
- Optimize hybrid data warehousing with instant, elastic and secure capacity
ETL/DI Managed Services
- Manage Tool Platform/Infrastructure
- Automate Management of operations for greater efficiency and responsiveness
- Scalability of Talend expertise
- 24/7 monitoring and support, software patches and upgrade management
Data Integration Project Implementation
- End to end project turnkey solutions
- Platform Upgrades
- ETL Platform Migrations (DataStage to Talend, Informatica to Talend etc.)
Training & Services
- Talend Certified instructor led Training
- Training onsite, online or in a public classroom, using cloud-based virtual machines with fully functional installations of Talend software
We shape our migration strategy around your goals to give you the smoothest end-to-end experience, offering best-of-industry data integration services powered by in-depth knowledge to solve your most complex technical challenges.
Certified Data Integration Platform Experts
80+ Migrations performed
Fast Track Implementation
Deep Technical expertise in New and Old platforms
Big data can mean big opportunities to transform your business, but a tailored approach in defining how data empowers decisions is key.
The onslaught of new technologies has created a massive uptick in the amount of information organizations collect, manage, and analyze. Together we can leverage new advanced tools or optimize your existing tech stack to extract actionable insights from diverse data generated in real time and at a large scale. Empower your organization with a holistic environment that can be used for modeling and predicting new market opportunities.
Strategy: Implementing advanced analytic solutions to maximize impact and ROI requires a holistic, interdisciplinary approach.
Readiness Assessment, Conceptualization and Roadmap:
We help you identify where you will benefit, as well as the value-gaining steps to take.
Proof of Concept:
Our PoC Pilot programs allow you to see big data in action, helping you make crucial decisions on rollouts or expansions. We can help you plan the entire implementation, build infrastructure plans, improve design clusters, and provide team training.
Our extensive experience working with big data technologies has resulted in successful implementations across multiple industries and many Hadoop distributions, such as Apache, Cloudera, Hortonworks, and MapR.
Big Data Infrastructure Advisory and Planning:
We collaborate with IT and key business stakeholders to reach milestones for implementation. Our infrastructure advisory and planning services are based on the following factors: Access, Capacity, Security, Latency and Cost.
Managed services: Our end-to-end Managed Services relieve you of time consuming and worrisome burdens while delivering the Big Data expertise required for assuring your data generates valuable, actionable ideas.
Our seamless management of your on-premise and cloud-based deployments ensure your Big Data applications and production environment run reliably and efficiently, 24×7.
Data Lakes: Let’s optimize your storage repository by providing scalability, versatility, schema flexibility and real-time ingestion to monetize structured, semi-structured and unstructured data.
We give you a roadmap to understand your business needs and create an architectural framework for your data.
We evaluate tools and technologies to find you the best solution.
We integrate existing datasets and tools into advanced techniques.
We facilitate a seamless adoption of data lake within your organization.
We provide enterprise grade authentication for data security.
Our technological expertise allows us to give our diverse clientele tailored-fit solutions.
Data preparation is the process of cleaning and transforming raw data before processing and analysis. This phase often involves reformatting raw data, making corrections, and combining data sets to enrich data.
Using the latest Databases, Data Warehouse, Cloud Data Lake and Data integration Technologies, we manage all phases of data engineering to ensure that all your applications are receiving high-quality data in order to meet all your performance expectations.
Our Data Preparation services
With data coming from various sources, we run multiple tests to ensure accuracy and reliability across all formats.
We ensure that all data is combined and converted to one format, compatible with that existing in data lakes and data warehouses.
Data Cataloging and Tagging
We catalog the data lineage and update the data dictionary, providing better governance and access to both new and existing data.
Preparation tools used at DataFactZ
Increasing your trust in data quality enables data driven decision making and timely insights. We provide you the resources to unlock immediate business value from data.
We have helped organizations across industries such as healthcare, educational institutes, retail, and nonprofits in their cloud migration to Snowflake and Azure.
Transform your data to a scalable, trustworthy data platform with the confidence of quality data integration.
Key Benefits of a Snowflake Migration
Performance and Speed
The elastic nature of the snowflake creates scalability in the virtual warehouse, allowing you to take advantage of extra compute resources.
Concurrency and accessibility
With traditional data warehouses, users can experience delays or failures when too many queries compete for resources. Snowflake addresses these concurrency issues with its unique multi-cluster architecture: Queries from one virtual warehouse never affect the queries from another, and each virtual warehouse can scale up or down as required. Users can efficiently obtain data without depending on other data intensive processes such as ETL/ELT loads.
Storage and support for structured & semi-structured data
Users gain the ability to combine structured and semi-structured data for analysis and load it into the cloud database, without the initial need for conversion or transformation into a fixed relational schema. Snowflake can automatically optimize how the data is stored and queried.
Seamless data sharing
Snowflake’s architecture enables data sharing among Snowflake users. It also allows organizations to seamlessly share data to consumers outside of the Snowflake environment through reader accounts that can be created directly from the user interface. This functionality allows the provider to create and manage a Snowflake account for a consumer.
Availability and security
Snowflake is distributed across availability zones of the platform on which it runs (AWS, Azure or Google Cloud) and is designed to operate continuously, tolerating component and network failures with minimal impact to customers.
What is a Data Warehouse?
A Data Warehouse is a system for managing data that supports business insights in a meaningful way through the collection of data from varied sources.
What are the Benefits of a Data Warehouse?
Data warehouses have four main benefits. You can create subject oriented analysis looking at a certain area or function, there is consistency in the data accessed from different sources, the data in the warehouse is stable and won’t change, and the warehouse analyses change over time.
What is a Cloud Data Warehouse?
A cloud data warehouse is the same as a data warehouse except it uses the cloud to access and store the data. This comes with additional benefits like cost managements, ease of use, and scalability.
What are the main types of Data Warehouses?
The main types of data warehouses are enterprise data warehouses providing support across an enterprise, operational data stores supporting reporting needs, and data marts that provide support for a specific line of business like sales.
What is Big Data?
Big data refers to large, complex growing amounts of data that cannot be handled by traditional data management solutions.
What are the main characteristics of Big Data?
Big data is defined by data with large amounts of volume, velocity, variety, veracity, value.
How is Big Data being processed?
Setting up big data is a complicated endeavor you have to develop a strategy to harness the data, identify important information like its sources, users, locations, and flow. You can then develop an infrastructure that will meet the analysis and computing needs of the data which is now ready to facilitate.
What are the benefits of Big Data?
Big Data provides better asset management, optimizes resources, more effective strategic planning, faster turnaround time, and shorter reaction times.
What is Data Integration?
Taking data from multiple sources and bringing them together to create a single source for a company to view the complete and current dataset, giving accurate access to business intelligence and data analysis.
How does Data Integration work?
The basis for data integration is taking data from multiple sources and making it accessible from a single location. There are multiple ways to approach this including Extract Transform Load (ETL), data virtualization, and physical data integration.
What are the main patterns of Data Integration?
There are five main patterns of data integration:
- ETL, Using extract transform load that converts raw data into a repository.
- Data Virtualization that gives real time access to data when requested by the user.
- Application Integration (API) syncs data between applications together that allow them to work together.
- Data Streaming that continuously moves data in real time to the repository
ELT, using Extract Load Transform to move data into a repository and then convert it to be understood by the source.
What is Data Integration used for?
Data integration is used for data governance, warehouse automation, data ingestion, data replication, marketing, internet of things, and data lake development.
What is Data Preparation?
Data Preparation is taking raw data cleaning and transforming it for business uses like analysis.
What are the benefits of Data Preparation?
The benefits of data preparation are data quality, increased scalability, error detection, efficient decision making, ease in collaboration, and future proofing.
What are the steps of Data Preparation?
The steps are collecting data, Assessing and contextualizing data, cleaning data, formatting and enriching data, and storing data.
Why is Data Preparation important?
It is important because it allows transparency of your data for auditing, accessibility for employees of different skill sets, and repeatability so you can continually prep data with ease.