The Ins and Outs of Databricks Data Quality and Validation

Does your organization use Databricks? If so, how familiar are you with Databricks’ data quality and validation features? Do you know how you can augment those built-in features with more robust Data Trustability/Quality monitoring from third-party providers like FirstEigen? 

Databricks is a popular cloud-based data storage solution used by numerous companies worldwide. Like many similar solutions, it can integrate with various third-party tools and services, including data quality and validation tools. When you use Databricks with the right third-party tools, you get a highly accurate solution for storing and managing your firm’s data. 

Quick Takeaways

  • Databricks is a cloud-based platform for data storage, management, and collaboration.
  • Databricks combines the best elements of data lakes and data warehouses in what the company calls a “Data Lakehouse.”
  • Databricks includes some basic features to check data quality.
  • Databricks’ built-in functionality can be augmented with third-party data quality monitoring tools to ensure the highest possible data quality.

What is Databricks?

Databricks is a cloud-based data storage, management, and collaboration platform. Its cloud-based nature makes it remarkably fast and easily scalable to meet a company’s growing data needs. It runs on top of existing cloud platforms, including Amazon Web Services (AWS), Google Cloud, and Microsoft Azure. 

Unlike other data storage solutions, Databricks combines the best elements of data lakes and data warehouses in what the company calls a Data Lakehouse. The Databricks Lakehouse Platform delivers the reliability and performance of a data warehouse along with the openness and flexibility of a data lake. 

Databricks’ unified approach eliminates data silos and simplifies traditionally complex data architecture. A Data Lakehouse can transform and organize ingested data and enable real-time queries and analysis. It can easily handle both real-time data streams and batched data ingestion. 

The Databricks workspace integrates a variety of functions and tools into a single interface. With Databricks, your company can:

  • Manage data processing workflows and scheduling
  • Ingest data from a variety of sources
  • Work in SQL
  • Create custom dashboards and visualizations
  • Manage data governance and security
  • Incorporate machine learning modeling, tracking, and serving

Databricks is used by companies of all sizes, from small businesses to large enterprises. Its customer base includes major players such as Apple, Atlassian, Disney, Microsoft, and Shell. 

These companies, along with hundreds of others, augment Databricks with a variety of third-party solutions. If your organization uses a different solution for data analysis, you can integrate Databricks with it. The platform highly supports open-source solutions, including Apache Spark and Redash.

Why is Data Quality Important for Databricks?

Trusted data of high quality is essential for all data management platforms, ensuring smooth and reliable operations, accurate reporting, and sound business decisions. 

To maximize the value from Databricks, you must ensure that you get the most accurate data. This is not an easy task, especially if data comes from multiple sources, including unmonitored external datasets and information streams (“Dark data”). While Databricks includes its own data monitoring functionality, it is enough to ensure you’re using reliably accurate data.

Understanding Databricks Data Quality and Validation

Inaccurate or error-ridden data can cause various issues and misinformed decision. 

The effects of poor data quality are not theoretical and can, in fact, be costly. Gartner reports that the average large organization loses $12.9 million a year due to data quality issues.Recognizing the need for high-quality data, Databricks focuses on six key metrics:

  • Accuracy
  • Completeness
  • Consistency
  • Timeliness
  • Uniqueness
  • Validity

The Databricks platform uses a variety of approaches to monitor these six aspects of data quality. 

Data Accuracy

Data accuracy is essential for data reliability, requiring data to be free from factual errors. 

Databricks employs three techniques to identify and remediate erroneous data:

  • Constraining and validating data to ensure all values exist and are true
  • Quarantining suspect data for future review
  • Flagging violations that fail validation checks

In addition, Databricks employs Time Travel, a feature that simplifies manual rollbacks to repair and remove any identified inaccurate data. Irreparable data can be vacuumed from data tables. 

Data Completeness

Data completeness ensures that all necessary data fields are present and available. Incomplete data can skew search results, resulting in misleading and partial analysis.

Databricks includes the following features that help ensure data completeness during the ingestion and transformation processes:

  • Atomicity – Guarantees that every write operation either completely succeeds or rolls back in the event of a failure caused by incomplete data
  • Enrichment – To establish relationships between data tables and their source files
  • Metadata management – Enables the addition of metadata to databases, tables, and columns

Data Consistency

Consistency compares similar data from multiple data sources. Inconsistencies often occur when updates to one data source are not made in another source. 

Databricks helps prevent inconsistencies by housing all data in a single Lakehouse. This creates a single source of truth and eliminates data silos that often result in out-of-synch data. 

Data Timeliness

Timeliness ensures that no data is out of date. Data that is too old is likely to be less accurate and reliable than fresher data. The Databricks Lakehouse helps address timeliness by accepting real-time data streams, which are more likely to be timely than older databases. 

Data Uniqueness

Uniqueness guards against duplicate data, which can skew data counts and analysis. Databricks employs multiple deduplication techniques, including merging data to update or delete duplicated data. Users can also employ the following deduplicating functions:

  • distinct() – to ensure that all rows in a table are unique
  • dropDuplicates() – to remove duplicate rows
  • ranking window – enables custom complex logic to locate duplicate data

Data Validity

Validity confirms that data conforms to a standardized format. Nonconforming data is more difficult or impossible to ingest and manage. 

Databricks offers four features that guard against invalid data:

  • Schema enforcement: Rejects data that does not conform to a table’s format.
  • Schema evolution: Enables users to overwrite a table’s format to accommodate changing data.
  • Explicitly update schema: Lets users add, reorder, or rename columns in a table.
  • Auto loader: Incrementally processes new data files as they are ingested into cloud storage.

Databricks data validation is a key part of the cloud-based platform. 

Improve Databricks Data Quality and Validity with FirstEigen’s DataBuck 

While Databricks’ built-in features do a good job of improving data quality, they can benefit from a comprehensive and more robust data monitoring functionality. FirstEigen’s Databuck Data Quality Module integrates with Databricks to autonomously monitor ingested data, detect data quality errors, and generate a trust score for all data assets. 

DataBuck uses artificial intelligence (AI) and machine learning (ML) to scan each data asset, detect data quality errors, and no manual coding is required. DataBuck also automates the tedious and time-consuming process of creating new rules and mechanisms to detect evolving data quality issues. 

If you use Databricks in your organization, you need DataBuck to ensure the highest data quality possible. It ensures that the data you use daily is accurate and complete. 

Contact FirstEigen today to learn more about data quality and validation in Databricks. 

Check out these articles on Data Trustability, Observability, and Data Quality.

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