Data Engineering

CLOUD9 OFFERS END-TO-END DATA LIFECYCLE MANAGEMENT — WE ENABLE BUSINESSES TO LEVERAGE DATA FROM TRANSACTIONAL SYSTEMS FOR ALL DATA CONSUMPTION PATTERNS

The challenge Businesses need an efficient way to source, interpret & consume data

Enterprises today need to be agile, even when saddled with legacy processes and complex multi-cloud infrastructure. Achieving that agility requires several things, such as:

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.

Services

End To End Engineering Services

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    DATA INGESTION

    Extraction of structured, unstructured data coming from streaming and batch sources and refining/cleansing data to make it available on legacy database systems or cloud systems, to data scientists and business users for exploration and analysis

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    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, type of data

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    DATA MODERNIZATION

    An efficient and smart approach for migrating business data to/from on-prem legacy systems into cloud storage infrastructure or new target platforms.

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     DATA PIPELINES

    Building production-grade replayable 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, etc., to process data in batch and real time

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    DATA CI/CD

    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

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    REAL-TIME PROCESSING

    Expertise in implementing real-time and batch data processing systems across distributed environments based on mobile, web hosting and cloud services

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