2 posts tagged with "Databricks"

In today's data-driven landscape, ensuring the reliability and accuracy of your data warehouse is paramount. The cost of not testing your data can be astronomical, leading to critical business decisions based on faulty data and eroding trust. 

The path to rigorous data testing comes with its own set of challenges. In this article, I will highlight how you can confidently deploy your data pipelines by leveraging Starlake JSQLTranspiler and DuckDB, while also reducing costs. we will go beyond testing your transform usually written in SQL and see how we can also test our Ingestion jobs.

The art of mastering data pipelines​

Mastering your data pipeline is a challenging art. A data pipeline generally contains the following phases:

• Collection: Extracting data from sources
• Ingestion: Loading the extracted data into the data warehouse
• Transformation: A phase that ultimately adds value to the collected data

The table below summarizes the tests run by Starlake on Load & Transform jobs:

Check to run on	Ingestion Test	Transform Test
Validate the filename pattern	✓
Validate the file structure (number and types of attributes, input file format - CSV / JSON / XML / FIXED-WIDTH)	✓
Check if loaded files or transform SQL SELECT statements  are materialized according to the defined strategy (APPEND / OVERWRITE / UPSERT_BY_KEY, SCD2 …)	✓	✓
Check for missing or unexpected records in the resulting table	✓	✓
Check if the resulting table has a correct schema	✓	✓
Check all expectations	✓	✓
Check time based query output with time freeze	✓	✓
The results of these automated tests are designed for both human review and CI/CD integration. For human review, a website is generated to help users easily identify failures and their causes. For CI/CD integration, a JUnit report is generated, and there is an option to specify a minimum coverage threshold. If the evaluated coverage falls below this threshold, the command will result in an error, and any failing tests will also trigger errors.

Untested SQL costs​

Thanks to data pipelines unit testing, we drastically reduce the development cost. Not running tests seems to allow high project's velocity, thereby delivering value quickly. However, the cost of a feature does not stop at its simple development but encompasses all the efforts put in until the feature's completion. Below are some hidden costs.

• Identifying bugs
  • Without unit tests, verifying development requires deploying the project. This deployment raises challenging questions about the deployment strategy and its rollback or correction procedures.
  • Verification might be carried out by a separate QA team, sometimes even outside the project team. This can lead to the use of feature flags to avoid deploying to production, complicating the implementation. Additionally, waiting for feedback from the QA team introduces delays, increasing the cost of fixing any bugs that arise.
  • Depending on the deployment strategy, verification may also be incomplete due to a lack of control over the test data used.
• Maintenance and Evolution Complexity
  • Many of us have faced a massive query and struggled to make modifications without disrupting existing functionality, all while aiming for improvements like optimizing processing time. Rigorous unit tests can help with this. They allow us to enhance the expected outcomes in current datasets, create new ones, and compare the modified query results with these expectations. This significantly reduces the risk of regression.
• Decreased productivity
  • The absence of automated tests often means manually re-running parts or all of the system to ensure correct integration, which can lead to spending time fixing collateral bugs and thus reducing overall productivity. As the project advances, more components need verification, making the process even more time-consuming. This significantly diminishes the willingness to refactor or revise code.
• Promoting expertise
  • Without unit tests, teams often assign the same tasks to the same people, which hinders skill development and increases the risk of knowledge loss due to turnover.
• Customer dissatisfaction
  • A project with uncertain product output quality often leads to dissatisfaction, frustration, and a loss of trust in the individual, the team, or the product.

We are all aware of the hidden costs associated with the absence of tests; in my opinion, these are the most significant. Therefore, we will explore how to manage a data pipeline.

Writing unit test in Starlake​

Suppose we have the following transform.

We test it by creating the following hierarchy:

This is then how it is executed

Starlake unit tests benefits​

Running tests on a local DuckDB database instead of the target Data Warehouse has the following advantages:

• Fast Feedback: Local execution is significantly faster than using a remote database due to network latency. Additionally, the local environment might be better suited for handling small volumes of test data.
• No Execution Cost: Depending on the pricing model of the target database, creating temporary resources and executing queries can incur both execution and storage costs.
• Setup and Cleanup of Automated Tests: Guarantee of resource isolation.
• Credential Issues: Running tests against a target database requires credentials, which may pose security risks.

Conclusion​

In this article, we have demonstrated how adopting unit testing, a crucial practice for software engineers, can significantly enhance the quality of our data pipelines. This approach not only reduces overall costs in the medium to long term but also ensures the maintenance of dynamic and enduring documentation. Additionally, implementing unit tests is essential for rigorous CI/CD processes, enabling seamless continuous data pipeline deployment.

If you encounter any issues while performing your tests locally, please report them on the Starlake GitHub repository. Your feedback is invaluable in improving local test coverage, empowering more data engineers to deploy their work confidently and smoothly. For further discussions and support, join our team on Slack.

We greatly appreciate your contributions. If you found this article helpful, please star the project on GitHub and share it on your social networks to help us reach a broader audience.

Introduction​

Polars is often compared to Spark. In this post, I will highlight the main differences and the best use cases for each in my data engineering activities.

As a Data Engineer, I primarily focus on the following goals:

1. Parsing files, validating their input, and loading the data into the target data warehouse.
2. Once the data is loaded, applying transformations by joining and aggregating the data to build KPIs.

However, on a daily basis, I also need to develop on my laptop and test my work locally before delivering it to the CI pipeline and then to production.

What about my fellow data scientist colleagues? They need to run their workload on production data through their favorite notebook environment.

This post addresses the following points:

• How suitable each tool is for loading files into your data warehouse.
• How easy and powerful each tool is for performing transformations.
• How easy it is to test your code locally before deploying to the cloud

Load tasks​

Data loading seems easy at first glance, as almost all databases and APIs offer some sort of single-line command to load a few lines or millions of records quickly. However, this simplicity disappears when you encounter real-world cases such as:

• Fixed-width fields: These files are typically exported from mainframe databases.
• XML files: I sometimes work in the finance industry where SWIFT is a common XML file format that will be around for some time.
• Multi-character CSV: For example, where the separator consists of two characters like ||.
• File validation: You cannot trust the files you receive and need to check their content thoroughly.

Loading correct CSV or JSON files using Spark or Polars is straightforward. However, it is also straightforward using your favorite database command line utility, making this capabilities somewhat redundant and even slower than the native data warehouse load feature.

However, in real-world scenarios, you want to ensure the incoming file adheres to the expected schema and load a variety of file formats not supported natively. This is where Spark excels compared to Polars, as it allows for the parallel loading of your JSONL or CSV files and offers through map operations local and distributed validation of your incoming file.

As XML and multichar and multiline CSV are only supported by Spark, dealing with file parsing for data loading, Spark is definitely the most suitable solution.

Transform tasks​

Three interfaces are provided to transform data: SQL, Dafarames and Datasets.

SQL

SQL is the preferred language for most data analysts when it comes to computing aggregations. One of its key benefits is its widespread understanding, which eliminates the learning curve.

You can use SQL with both Spark and Polars, but Polars has significant limitations. It does not support SQL Window functions nor does it support the SQL statements for update, insert, delete, or merge operations.

Dataframes

Both Polars and Spark offer excellent support for DataFrames. The main added value of using DataFrames is the ability to reuse portions of code and access features not available in standard SQL, such as lambda functions, JSON handling and array manipulation.

Datasets

As a software engineer, I particularly appreciate Datasets. Datasets are typed DataFrames, meaning that syntax, column names, and types are checked at compile time. If you believe that statically typed languages greatly enhance code quality, then you understand the added value of datasets.

Datasets are only supported by Spark, allowing you to write clean, reusable, and statically typed transformations. They are available exclusively to statically typed languages such as Java or Scala.

Spark stands out as the only tool with complete support for SQL, DataFrames, and Datasets. Polars’ limited support for SQL makes it less suitable for my data engineering tasks.

Cloud Era​

At least 60% of the projects I have worked on are cloud-based, primarily targeting Amazon Redshift, Google BigQuery, Databricks, Snowflake, or Azure Synapse. All of these platforms offer serverless support for Spark, making it incredibly easy to run workloads by simply providing your PySpark script and letting the cloud provider handle the rest.

In the cloud environment, Spark is definitely the tool of choice as I see it.

Single node​

There has been much discussion about Spark being slow or too heavy for single-node computers. I was particularly interested in running this test since I currently execute most of my workloads on a single-node Google Cloud Run job with Spark embedded in my Docker image.

I decided to conduct this test on my almost 3-year-old MacBook Pro M1 Max with 64GB of memory. The test involved loading 27GB of CSV data, selecting a few attributes, computing metrics on those selected attributes, and then saving the results to a Parquet file.

I ran Spark with default settings and without any fine-tuning. This means it utilized all 10 cores of my MacBook Pro M1 Max but only 1 gigabyte of memory for execution.

::: note I could have optimized my Spark workload, but given the small size of the dataset (27GB of CSV), it didn't make sense. The default settings were sufficient. :::

Here are the results after a cold restart of my laptop before each test to ensure the test did not benefit from any operating system cache.

• Apache Spark pipeline took: 29 seconds

• Polars pipeline took: 56 seconds

::: note Rerunning Polars gave me 23 seconds instead of 56 seconds. This discrepancy is probably due to filesystem caching by the operating system. :::

Load and Save Test: Load a 27GB CSV file with 193 columns per record and save the result as parquet.

• Apache Spark pipeline took: 2mn 18s

• Polars pipeline took: 2mn 32s

Load parquet and filter on column value then return count : Load a 74 millions records parquet file with 193 columns, filter on 'model' column and return count.

• Apache Spark pipeline took: 3 seconds

• Polars pipeline took: 28 seconds

The table below summarises the results

Task	Spark 1GB memory	Polars All the available memory
Load CSV, aggregate and save the aggregation as parquet	29s	56s
Load CSV and Save parquet	2mn 18s	2mn 32s
Load Parquet, filter and count	3s	28s

Conclusion​

I don’t think it is time to switch from Spark to Polars, at least for those of us accustomed to the JVM, running workloads in the cloud, or even working on small datasets. However, Polars may be a perfect fit for those familiar with pandas.

As of today, Spark is the only framework I see that can handle both local and distributed workloads, adapt to on-premise and cloud serverless jobs, and provide the complete SQL support required for most of our transformations.