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Data quality and reliability for the cloud – Azure, AWS & GCP

A broad spectrum of tools is re-imagining the problem of monitoring the modern data ecosystems on the cloud.

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The holy grail of “trust in data” from data to insight journey of enterprises is not entirely new. Since BI and analytic workloads are separated from data warehouses, the chasm has widened.

There’s an even larger gap between what business needs, business operations supported by the IT application landscape, and the reliability of the data accumulated in the data warehouses for the business teams.

Concepts & tools emerged to address the gap in the form of:

  • Golden record for every business entity that is of interest. 
  • Building on it was master data management – standardizing the glossary on how data is understood, organized, and governed, supported by vendors like IBM, Informatica, and Talend. 
  • It attempted to tame the chaos by standardization by inventing business glossaries and tons of ETL tools to support the business rules to help businesses make sense of the data. 

In this mayhem, data quality solutions and tools were buried deep in MDM and data governance initiatives. Still, two challenges existed – The first was to look into the past while asking whether data was trustable.

Second, ‘quality’ was measured with respect to the golden record and master data – standardization, which itself was constantly evolving.

Data reliability on the cloud  – Why & what has changed?

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While the big data hype started with Hadoop, concerns with volume, velocity, and veracity were tackled, this remained an enterprise play.

True innovation kick-started with MPP systems like Redshift on AWS built cloud natively, which guaranteed a higher performance to handle massive datasets with good economics and a SQL-friendly interface. 

This, in turn, spurred a set of data ingestion tools such as Fivetran, which made it easier to bring data onto the cloud.

Evolution of data infrastructure and modern data ecosystem on the cloud

Today, data is getting stored in data lakes on cloud file systems and cloud data warehouses, and we see this reflected in the growth of vendors like Databricks and Snowflake.

The dream of being data-driven looked much closer than before. 

Business teams were hungry to analyze and transform the data to their needs, and the BI tool ecosystem evolved to create the business view on data.

The facet that changed beneath and along this evolution is that data moved from a strictly controlled and governed environment to the wild west as various teams are transforming and manipulating data on the cloud warehouses.

Evolution of data teams and data engineering-dependent business teams

It’s not just the volume and growth of data. The teams hungry for data (data consumers) have also exploded in the form of BI teams, analytic teams, and data science teams.

In fact, in the digital native organizations (which were purely built on the cloud), even the business teams are data teams. E.g., a marketeer wants real-time information on product traffic to optimize campaigns. 

Serving these specialized and decentralized teams with their requirements and expectations is not an easy task.

The data ecosystem responded with a clever move, marking the beginning of data engineering and pipelines as a basic unit to package the specialized transformations, joins, aggregations, etc.

The reality is that the data teams are constantly fighting the battle of broken pipelines, changing schemas, and formats, that affect all data consumers like damaged BI dashboards and garbage predictions from ML models.

This calls for new thinking around creating trust in the data, erstwhile data quality metrics and approaches are insufficient.

We need data reliability metrics to monitor and observe the changes in the data in all shapes (e.g., distributions) and forms (schema changes, format changes) and the ones that serve the needs of BI engineers/analysts and data scientists.  

Key factors aiding data reliability adoption among smaller enterprises on the cloud

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As enterprises move towards self-serving tools for, business intelligence (BI), data analysis, broken dashboards, and drifting machine learning models can be painful for enterprises of all sizes.

In fact, the problem is accentuated for enterprises with smaller data teams as they spend a lot of time-fighting data reliability issues, which otherwise could be utilized to unlock the value of the data.

This also calls for a more economical way that delivers engineering efficiencies based on cloud-native architecture, optimized and scaling on-demand compute and storage for data reliability monitoring to be delivered.

No-code data quality to the rescue of business teams

While significant progress has been achieved in bringing data closer to the business teams, there remains an unsolved gap in the modern data ecosystem.

The current tools bring the capability, they also expose the underlying complexity of the data infrastructure directly to business teams.

Most enterprises find it challenging to get started with using the cloud because there aren’t many low-code tools that make it easy to work with data.

These tools often have a good abstraction of the complexity of data, but they don’t always have a user interface that is aligned to the specific goals and purposes of the users.

This area is picking up steam, and we are seeing new groups bringing the no-code/low code in the data reliability area.

New tools to effectively monitor data Infra, data pipelines & data quality+reliability 

A broad spectrum of tools is re-imagining the problem of monitoring the modern data ecosystems on the cloud.

Data Dog & New Relic-like tools monitor the data infrastructure on the cloud. Other tools like Unravel, monitor data stacks on the cloud.

There are also tools emerging to monitor data pipelines on the cloud. And finally, Qualdo-DRX is a leading tool to monitor data quality and reliability, available exclusively and re-imagined for all public clouds. 

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