Data Integration brings new perspective to the traditionally slow and error-prone ETL process. We leverage proprietary technology which accumulates schema intelligence over the history of engagements. This central schema dictionary accelerates schema matching, and compartmentalizes workflow tasks. The direct effect is a streamlined engineering process and parallelization of collaborative efforts, resulting in faster completion and improved data quality.

Data Integration - Streamlined Engineering
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The statistics of data integration projects WITHOUT speak for themselves:

Statistical Data without's Data Integration Tehnology

Source: Bloor Research

Hadoop-based Data integration Technology

Our engineering incorporates the latest in-memory Big Data technology, delivering high-performance execution of ETL code. This Hadoop-based technology has built-in resilience and concurrency, and can run off both directly programmed code or UI-based configuration instructions. It scales linearly on commodity nodes, and therefore meets any volume requirements, no matter how gargantuan the feed.

More often than not, data integration projects fail on lack of emphasis on metadata management and executive-sponsored data governance. All data sources are backed by a rich set of metadata, which captures data context (schema, format, semantics, etc.), and data lineage (origin of data and processing steps applied during transfer).

As a rule, data context is not formally documented, and exists only as “tribal knowledge”. It is a technical debt that leads to quality issues, as well as protracted implementation times for new data sources and consumers. Data lineage captures how data travels through diverse processes and facilitates backward error tracing to their sources. Data analysts use this capability to replay specific portions of the dataflow for debugging or regeneration. Missing data context and lineage account for a high percentage of difficulties encountered downstream.

Data governance covers all aspects of data quality and access control. It is a system of access rights and accountability for managing enterprise data assets. The data practice incorporates processes, roles, standards and metrics to maximize data quality at the root source, safeguarding rules for duplication, completeness, freshness, etc., and ensures compliance and security. We recommend that senior management implements and sponsors change to pave the way for effective enterprise-wide data governance policies, with appropriate delegation and accountability.

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