Choosing a modular, flexible and scalable data integration tool is a critical success factor in achieving tangible outcomes from advanced data analytics within a short window of opportunity. Businesses often struggle to derive insights from data lakes that are poorly governed and managed. Infosys Nia Data leverages the best-of-breed open source technologies and offers a suite of seamlessly integrated offerings for gathering, searching and surfacing knowledge from diverse data sources.
A leading chocolate and confectionary manufacturer in US wanted to implement a data lake to lay the foundation for driving big data analytics and data driven decision making.
Nia Data was implemented as the data lake solution and data ingestion processes were established. Ingested data was validated, transformed and harmonized through IIP models. This harmonized data was made available for analytics. Data exploration and reporting was performed on the harmonized data to provide business insights.
The client was able to validate business hypothesis quickly within weeks to run multiple parallel experiments. Business users were able to perform analytics and derive business insights that could be used for driving business actions.
Supports multiple data sources, complex data transformation processes and integrated environment
Supports an intuitive drag-&-drop web interface for iterative development of data integration flows; enabling incremental steps with ability to evaluate transformation output at each step before
Supports looping, branching and conditional constructs. Extensibility to write own Java, Scala snippets to build complex pipelines
Enhanced support for cloud data Sources / storage such as Azure ADLS, Amazon S3, Amazon RDS, Azure SQL or streaming data over AWS Kinesis. Full support for compute workloads on native cloud distributi
Build on top of the open-source Hadoop framework and leverage innovations in the latest Spark engine for data ingestion, transformation and stream processing. Elastic capabilities to scale with custom
Invoke pipelines with on-demand and trigger-based scheduling. Visually monitor pipeline activity with logging and pipeline history and track errors
AI and Data Science the ability to scale and accelerate data engineering and data science disciplines is a key while achieving organizational goals on overall business transformation.
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