Introduction
In today's data-driven world, ensuring the quality of your data is paramount. Poor data quality can lead to erroneous insights, skewed analyses, and ultimately, flawed decision-making. Undetected errors or invalid data can mislead decision-makers, create missed opportunities and prevent compliance with regulations. To address this challenge, data engineers and analysts rely on robust data quality checks to validate and cleanse their data pipelines. Among the myriad of tools available, Databricks stands out as a powerful platform for managing big data and performing analytics at scale.
In this comprehensive guide, we'll walk you through the process of setting up and leveraging data quality checks in Databricks to maintain the integrity and reliability of your data pipelines.
Understanding Data Quality Checks
Before diving into the technical aspects, let's establish a clear understanding of what data quality checks entail. Data quality checks are systematic procedures used to assess the accuracy, completeness, consistency, and integrity of data. These checks are essential for identifying anomalies, errors, or inconsistencies within datasets, thereby ensuring that the data meets predefined quality standards.
Databricks Data Quality Framework (Image Source: Databricks)
Setting Up Your Databricks Environment
To get started, you'll need access to a Databricks workspace. If you don't have one already, you can sign up for a free trial or use an existing account. Once logged in, create a new Databricks notebook where you'll write and execute your data quality checks.
Step 1: Data Profiling
Before implementing data quality checks, it's crucial to understand the structure and characteristics of your datasets. Data profiling helps you gain insights into the distribution of data values, identify missing or erroneous values, and detect outliers.
Constraints is a good way to enable the analytics platform to identify a dataset that contains errors and prevents it from being inserted into the table. Two types of constraints are supported with Delta tables:
- NOT NULL: Prevents any NULL values from being inserted into the column
- CHECK: Requires that the specified Boolean expression must be true for each input row
When a constraint is violated for any row, the entire transaction will fail, raise an error and roll back.
In your Databricks notebook, use Spark SQL or Python libraries like Pandas to perform data profiling tasks such as:
- Descriptive statistics (mean, median, min, max, etc.)
- Counting null or missing values
- Identifying unique values and their frequencies
- Visualizing data distributions with histograms or box plots
Step 2: Define Quality Metrics
Next, define the quality metrics that align with your data quality objectives. These metrics will serve as benchmarks for evaluating the quality of your data. Common quality metrics include:
- Completeness
Completeness means that all the data needed for a use case or project is present and available. This can be very large depending on the size of the data source and number of tables. We can use atomicity, enrichment and metadata management in order to ensure that we are maintaining the completeness of the data.
- Accuracy
Data is considered accurate if it validates all the factual information coming from the business and the various data sources. Accuracy is an important metrics as that gives confidence to the business users that their data is correct and can be used. Databricks provides three techniques that can be used to ensure accuracy. Constraints and Validate, Quarantine data and Flagging violations are the techniques that help us do that. Besides that it also provides time travel and vacuum features in order to remove an inaccurate version and help maintain accuracy.
- Consistency
There are two major aspects to the consistency feature of data quality:
- Data values used by data consumers do not conflict with each other. For instance, the value ‘net revenue’ will return the same result when queried by analysts from table_a or by data scientists from table_b
- The correct data is returned to the user, regardless of concurrent read or write processes affecting the relevant data objects
Both aspects help ensure that users will always receive consistent data from across any source in the system.
- Timeliness
Timeliness means the freshness of data and that the data should always be up to date. The timeliness of data would actually depend on the use case primarily but the target should be to minimise the time between the actual change in the source and its reflection on the analytical system.
- Validity
Validity means that the data should conform to a certain format. Besides the features discussed in consistency, the validity will be able to use some of Databricks features such as schema enforcement, schema evolution and overwrite, explicitly updating the schema and Auto Loader. These features help in order to fulfil specific use cases highlighted by the clients.
- Uniqueness
Uniqueness is a crucial dimension as well as that ensures that the duplicate data is neither processed nor presented to the stakeholder. Duplicate data can lead to misleading insights which in turn will result in wrong decisions. Merge, drop duplicate, merge and ranking windows are certain techniques which are provided by Databricks in order to handle the issue.
Based on your specific use case and requirements, customise these metrics to suit your needs.
Step 3: Implement Data Quality Checks
With your quality metrics defined, it's time to implement data quality checks in your Databricks environment. You can achieve this using SQL queries, Spark DataFrame operations, or custom Python scripts.
For example, you might write PySpark:
- Count the number of null values in each column.
- Calculate the percentage of outliers beyond a certain threshold.
- Verify data integrity constraints such as referential integrity or uniqueness.
from pyspark.sql.functions import col, isnull # Example Check for Completeness def check_completeness(df, column_name): return df.filter(isnull(col(column_name))).count() == 0 # Example Check for Validity def check_validity(df, column_name, regex_pattern): return df.filter(~col(column_name).rlike(regex_pattern)).count() == 0 # Example Check for Uniqueness def check_uniqueness(df, column_name): return df.groupBy(column_name).count().filter(col("count") > 1).count() == 0
We can also check for integrity while the ETL pipelines are run:
# Load DataFrame df = spark.read.format("delta").load("/path/to/delta/table") # Apply checks if check_completeness(df, "required_column") and \ check_validity(df, "email_column", r"^[a-zA-Z0-9+_.-]+@[a-zA-Z0-9.-]+$") and \ check_uniqueness(df, "id_column"): print("Data Quality Checks Passed") else: print("Data Quality Checks Failed")
Alternatively, you can leverage Spark's DataFrame API or third-party libraries like Great Expectations to define and execute data validation rules programmatically.
Step 4: Automate Monitoring and Alerting
To maintain ongoing data quality, it's essential to automate the monitoring and alerting process. Schedule your data quality checks to run at regular intervals (e.g., daily, weekly) using Databricks Jobs or Apache Airflow. Configure alerts to notify stakeholders via email, Slack, or other communication channels whenever anomalies or issues are detected.
import logging # Configure logging logging.basicConfig(level=logging.INFO) logger = logging.getLogger(__name__) # Log the results of checks if check_completeness(df, "required_column"): logger.info("Completeness check passed") else: logger.error("Completeness check failed")
Databricks has automated monitoring and it creates dashboards as well automatically. You can just create the dashboard in the Catalog Explorer section of Databricks.
Step 5: Continuous Improvement
Data quality management is an iterative process. Continuously monitor the performance of your data quality checks and refine them as needed. Solicit feedback from data consumers and stakeholders to identify areas for improvement and enhance the effectiveness of your data pipelines.
Conclusion
By following this step-by-step guide, you can establish robust data quality checks within your Databricks environment to validate and cleanse your data pipelines effectively. Remember, maintaining high-quality data is not a one-time task but an ongoing commitment that requires vigilance, automation, and continuous improvement. With Databricks, you have the tools and capabilities to ensure the integrity and reliability of your data assets, empowering your organisation to make informed decisions with confidence.
