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Cumulative Data

Is your data team bogged down by broken processes and quality issues?

Unlock their potential to drive insights and enhance AI/ML capabilities  with Data Management Platform.

Implementing best practices transforms your data operations

Challenges in Data Quality and Reliability

Many challenges in data quality and reliability can be traced back to fundamental issues in data processing, such as:
  • Data Corruption: Occurs from simultaneous writes or overwriting data.
  • Data Incompleteness: Results from overwriting valid data, creating gaps.
  • Incomplete Data Utilization: Happens when scheduled jobs do not consider the actual state of input data, only the time.
  • Inaccurate Date Inputs: Scheduled jobs often fetch the wrong datasets because they do not thoroughly verify the data state.
  • Cascading Failures: Scheduled jobs fail to verify all input data aspects, leading to a domino effect of issues.
  • Duplicate Runs: Cause data redundancy through repeated rows or updates.

Best Practices for Enhanced Data Reliability

To address these issues effectively, adopting a robust framework for data handling is crucial. Here’s how Trel transforms data management.

In Trel, each dataset is immutable. Once a dataset is generated, it remains unaltered, ensuring it resides in a pristine state. This method eliminates data corruption from concurrent writes and isolates issues from job failures, as incomplete or corrupted datasets do not enter the operational dataset catalog.

Key Benefits:

  • Eliminates data corruption: All jobs write to new, dedicated locations.
  • Reduces inconsistency: Corrupted data from failed jobs remain isolated, preserving the integrity of other datasets.

Unlike traditional data catalogs that handle basic table management, Trel catalogs dataset snapshots with their reference timestamps. This advanced approach ensures that users and automated processes access the correct snapshot, independent of the query time.

Key Benefits:

  • Accurate data retrieval: Aligns job inputs with the correct timestamps to ensure precision.
  • Avoids cascading errors: Maintains data integrity through delays or issues, preventing erroneous data distribution.
Trel’s jobs are designed to be idempotent, consistently delivering the same outcomes, regardless of multiple executions. This consistency is achieved by ensuring jobs time-travel to the snapshot with the correct reference timestamp, rather than the most recent one, while also creating immutable datasets.

Key Benefits:

  • Prevents data redundancy: Ensures that repeated job runs do not impact the data’s integrity or accuracy.

Enhanced Data Reliability with Trel

Imagine a world where your data team no longer has to chase down data inconsistencies, allowing them to focus on generating valuable insights and advancing technological capabilities. Trel’s approach to immutable datasets, sophisticated cataloging, and idempotent processing not only streamlines operations but also drastically improves data reliability.

Discover How Trel Can Transform Your Data Operations

Empower your data processes with Trel’s innovative data reliability solutions. Transition from a cycle of data repair to a streamlined process where data integrity propels your company towards success.
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At Cumulative Data, we understand the challenges that come with managing complex data systems.

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