Data-Engineering - Tuteck Technologies

Services – Data And Analytics – Data Engineering

TuTeck provides comprehensive data lifecycle management tailored to data consumption patterns.

Businesses require streamlined and adaptable methods for sourcing and utilizing data

Enterprises today grapple with vast data volumes across multi-cloud infrastructures. However, realizing the potential of this data is hindered by mounting complexity, inadequate data management, and outdated infrastructure and tools.

Overcoming these challenges necessitates:

Transforming expansive enterprise data into actionable insights swiftly.
Establishing a reliable data foundation with robust analytics capabilities, drawing insights from diverse data sources.
Conducting thorough data preparation to extract insights from raw data, enabling various analytics types—from interactive visualizations to predictive and prescriptive analytics—in context-specific patterns.

WHAT WE DO

We provide data engineering services that propel digital transformation forward

TuTeck empowers enterprises to tackle data challenges, enhance end-user satisfaction, and shape business strategies through insightful intelligence. Our data engineering teams analyze structured, semi-structured, and unstructured data using advanced technology and processing tools. We offer comprehensive data lifecycle management services, replacing fragmented in-house data infrastructure with agile, cost-effective solutions that transform big data pipelines into resilient systems ready for dynamic business analytics.

What We Offer

TuTeck Supports the enterprises during their End-To-End Data Life Cycle Management

Collection & Synthesis of Data

Extracting structured and unstructured data from both streaming and batch sources, refining and cleansing it, and making it accessible on legacy databases or cloud systems for exploration and analysis by data scientists and business users.

Storage and ELT / ETL processes

Extracting, processing, transforming, and loading data into diverse relational, non-relational, NoSQL, and big data systems or cloud storages, based on data availability, volume, velocity, and type.

Modernization and migration of data

A streamlined and intelligent method for migrating business data between on-premises legacy systems and cloud storage infrastructure or new target platforms.

Data pipelines development

Developing robust, scalable data workflow pipelines for seamless movement, transformation, and storage of data.

Utilizing a range of legacy, Big Data, and cloud orchestration tools such as AWS Glue, EMR, Azure Data Factory, Databricks, Synapse, Informatica, Talend and others to handle data processing in both batch and real-time scenarios.

Continuous integration and deployment

Proficiency in deploying legacy and cloud-based services to create streamlined production build and release pipelines.

This includes infrastructure-as-code artifacts, application data, database objects (schema definitions, functions, stored procedures, etc.), data pipeline definitions, and logic for data quality, validation and transformation.

Real-time distributed data processing

Leveraging advanced technologies and frameworks such as Apache Kafka, Apache Flink, Apache Spark Streaming, Kinesis, we enable seamless ingestion, processing, and analysis of streaming data from diverse sources.

Our solutions ensure low-latency data processing, real-time analytics, and efficient handling of high-volume data streams.

With scalable architectures and robust data pipelines, we facilitate real-time decision-making, predictive analytics, and operational insights, empowering businesses to stay competitive in dynamic markets

HOW WE DELIVER

TuTeck pioneers on delivering next-generation data platforms equipped with actionable data strategies

TuTeck employs a consultative method integrating data engineering, cloud solutions, data privacy, and compliance expertise alongside proprietary maturity models to build cutting-edge data ecosystems. Our adaptable resourcing model enables rapid team scaling using.

Assessment & Planning

Conduct a thorough assessment of current data landscape, business goals, and regulatory requirements. Develop a comprehensive data strategy aligned with organizational objectives.

Deliverables:

Data inventory and assessment report.
Prioritized list of business use cases.
Draft data governance framework.

Timeline: 6 weeks to 8 weeks

Design & Architecture

Design the architecture for the data ecosystem, including data storage, integration, and analytics platforms. Define data flows and security measures.

Deliverables:

Detailed data architecture diagrams.
Data integration plan and protocols.
Security and compliance framework.

Implementation and Integration

Implement the designed data architecture. Implement data governance policies and integrate data sources and systems.

Deliverables:

Implemented data platforms and systems.
Data governance policies and procedures.
Initial data integrations completed.

Testing and Optimization

Test the implemented systems for performance, security, and data quality. Optimize processes and workflows based on feedback and testing results.

Deliverables:

Test reports and performance metrics.
Optimized data workflows and processes.
Data quality assurance framework.

Deployment and Training

Deploy the finalized data strategy across the organization. Conduct training sessions for stakeholders and end-users on using new data tools and platforms effectively

Deliverables:

Deployed data platforms and systems.
Training materials and sessions conducted.
User adoption and feedback reports

Monitoring and Continuous Improvement

Establish monitoring mechanisms to track data usage, performance, and compliance. Continuously improve data strategy based on insights and evolving business needs.

Deliverables: