Why Every Pro Sports Team Needs a Data Engineer

Do you work with performance data and recognize these 5 challenges?

Isolated data (CSV, XLSX, JSON, Parquet) Files
Scattered Codebase
Slow Data Ingestion
Refetching Data on Every Run
Inadequate Backup and Disaster Recovery

At Eyedle we believe that data engineering plays a vital role in the sports industry by transforming raw data into high-quality information that teams and coaches can use to make strategic decisions. We use data engineering and software engineering to reduce the time to insight and improve data quality.

Isolated data (CSV, XLSX, JSON, Parquet) Files

Challenge: Essential data is scattered across multiple CSV files, often stored on personal laptops.

Pain Point: Data silos and restricted access create inefficiencies and inconsistencies, preventing seamless collaboration and making data governance difficult.

How to solve it:

1. Implement a Data Warehouse/Data Lake/Data Lakehouse: Migrate isolated files (CSV, XLSX, JSON, Parquet) to a centralized data storage solution such as:
  - Data Warehouse (e.g., AWS Redshift, Google BigQuery, Snowflake)
  - Data Lake (e.g., AWS S3 with Lake Formation, Azure Data Lake, Delta Lake)
  - Data Lakehouse (using Apache Iceberg for example)
2. Access Control:
  - Implement access control using proper roles or group policies.
  - Utilize table- or column-level security for sensitive data.
3. Data Catalog:
  - Create a data catalog using tools like AWS Glue, Apache Atlas, or Azure Purview.
  - Document datasets, schema, and lineage for better governance.
4. ETL Pipelines:
  - Build ETL pipelines using tools like Apache Airflow, Dagster, AWS Glue, or Azure Data Factory.
  - Perform data validation, normalization, and deduplication in these pipelines.

Scattered Codebase

Challenge: Code is spread across numerous disorganized files, frequently named “preprocess_match_data_may.py", “scratch.py” etc.

Pain Point: Lack of version control and documentation makes it hard to understand, maintain, and scale existing code, slowing down analysis and innovation.

How to solve it:

1. Version Control:
  - Use a centralized repository with Git (GitHub, GitLab, Bitbucket).
  - Organize projects into repositories based on business domains or microservices.
2. Project Structure:
  - Follow a clear structure:
    
    └── project
    ├── README.md ├── requirements.txt ├── src │ └── data_ingestion.py └── tests └── test_data_ingestion.py
  - Create sub-modules and sub-packages for reusable code.
3. Documentation:
  - Write clear documentation using tools like Sphinx, MkDocs, or Doxygen.
  - Include docstrings in Python files adhering to PEP257.
4. Code Quality and Standards:
  - Implement code quality checks using linters (e.g., Flake8, Pylint) and formatters (e.g., Black).
  - Use testing frameworks like PyTest or Unittest.

Slow Data Ingestion

Challenge: Data ingestion pipelines are not optimized, leading to high latency in accessing data from various sources.

Pain Point: Analysts and data scientists struggle with retrieving large volumes of data, resulting in delayed insights and suboptimal decision-making.

How to solve it:

1. Parallel Processing:
  - Use multithreading with Python’s threading module.
  - Use multiprocessing with Python’s multiprocessing module.
  - Use asynchronous programming with Python’s asyncio module.
2. Incremental Loading:
  - Implement change data capture (CDC) or watermarking to load only new/modified data.
3. Partitioning and Clustering:
  - Partition large datasets by date or business logic for optimized queries that run way faster.
4. Streaming Data:
  - Use Apache Kafka or AWS Kinesis to stream real-time data for analysis.

Refetching Data on Every Run

Challenge: Scripts are set up to refetch data from the source every time they are executed.

Pain Point: Redundant data fetching not only increases processing time but also leads to unnecessary costs and server load.

How to solve it:

1. Data lake:
  - Instead of refetching data from the data provider every time, periodically (daily or hourly) pull the changed data from the API and store it in your own data lake.
2. Data Caching:
  - Cache data results using a distributed cache like Redis.
3. Incremental Scheduling:
  - Use orchestration tools to schedule incremental jobs.
  - Implement triggers or interval-based schedules to avoid redundant refetching.
4. Job Optimization:
  - Identify bottlenecks with profiling tools and good logging
  - Optimize long-running queries with indexes or partitions.

Inadequate Backup and Disaster Recovery

Challenge: Data backup and disaster recovery are not adequately planned or executed.

Pain Point: This lack of preparedness can result in significant data loss and prolonged downtime in the event of a disaster.

How to solve it:

1. Backup Policies:
  - Define RTO (Recovery Time Objective) and RPO (Recovery Point Objective) for critical data.
  - Create periodic backup jobs for datasets using cloud services or open-source tools.
2. Replication:
  - Set up cross-region replication for storage solutions (S3, Azure Blob Storage).
  - For databases, implement read replicas or backups to secondary regions.
3. Automated Alerts:
  - Configure alerts for backup job failures using CloudWatch, Azure Monitor, or Prometheus.

At Eyedle we believe that data engineering plays a vital role in the sports industry by transforming raw data into high-quality information that teams and coaches can use to make strategic decisions. We use data engineering and software engineering to reduce the time to insight and improve data quality.

By addressing the above pain points through structured data engineering practices, we can ensure robust, efficient, and scalable data management that empowers data scientists and analysts to generate actionable insights confidently.

If you want to have a chat about sports analytics data engineering (SPADE) feel free to reach out. We’d love to share some experiences!