ETL Tooling And Implementation

Why this skill matters for ETL Developers

ETL Tooling and Implementation turns raw data into reliable, analytics-ready datasets. As an ETL Developer, you will choose tools, design jobs, manage environments, deploy changes, and keep pipelines healthy. Mastering this skill lets you deliver stable data products, reduce incidents, and scale workloads confidently.

Who this is for

• Aspiring ETL Developers moving from SQL or analytics into production-grade pipelines.
• Data Engineers who want stronger practices around orchestration, deployment, and monitoring.
• BI/Analytics Engineers who need dependable data flows feeding dashboards and models.

Prerequisites

• Comfortable with SQL joins, window functions, and basic DDL/DML.
• Basic scripting in Python or similar (loops, HTTP requests, file IO).
• Familiarity with data formats: CSV, JSON, Parquet; and basics of REST APIs.
• Understanding of source control (Git) and environment separation (dev/stage/prod).

Learning path

• Start with tool basics: navigate the UI/CLI, create a simple pipeline, run and observe logs.
• Add SQL transformations and idempotent loading patterns (upserts/merges).
• Automate tasks with scripts, parameters, and simple schedulers.
• Connect to files and APIs, handle pagination and schema drift safely.
• Introduce logging/audit tables, data quality checks, and row-count validation.
• Configure dev/stage/prod with config separation and secrets management.
• Practice deployment basics: versioning, migrations, rollbacks.

Practical roadmap with milestones

Worked examples

-- Generic SQL (adapt to your warehouse dialect)
MERGE INTO core.customers AS tgt
USING (
  SELECT c.*,
         ROW_NUMBER() OVER (PARTITION BY customer_id ORDER BY update_time DESC) AS rn
  FROM staging.customers c
) AS src
ON tgt.customer_id = src.customer_id
WHEN MATCHED AND src.rn = 1 THEN
  UPDATE SET name = src.name,
             email = src.email,
             update_time = src.update_time
WHEN NOT MATCHED AND src.rn = 1 THEN
  INSERT (customer_id, name, email, update_time)
  VALUES (src.customer_id, src.name, src.email, src.update_time);

WITH wm AS (
  SELECT COALESCE(MAX(watermark_value), TIMESTAMP '1970-01-01') AS last_wm
  FROM meta.watermarks
  WHERE job_name = 'orders_ingest'
)
SELECT *
FROM raw.orders r
CROSS JOIN wm
WHERE r.update_time > wm.last_wm;

import os, time, requests
API = os.environ.get('API_URL')
TOKEN = os.environ.get('API_TOKEN')
page = 1
results = []
while True:
    resp = requests.get(API, params={'page': page}, headers={'Authorization': f'Bearer {TOKEN}'})
    if resp.status_code == 429:
        time.sleep(2 ** min(page, 5))
        continue
    resp.raise_for_status()
    data = resp.json()
    results.extend(data['items'])
    if not data.get('next_page'):
        break
    page += 1
# write results to a landing file
with open('/data/landing/api_items.jsonl', 'w') as f:
    for item in results:
        f.write(f"{item}\n")

-- Pseudocode in SQL dialect
WITH expected AS (
  SELECT 'id' AS col UNION ALL
  SELECT 'name' UNION ALL
  SELECT 'email'
),
actual AS (
  SELECT column_name AS col FROM information_schema.columns
  WHERE table_schema = 'staging' AND table_name = 'customers_incoming'
)
SELECT a.col AS unexpected_col
FROM actual a
LEFT JOIN expected e ON a.col = e.col
WHERE e.col IS NULL;

CREATE TABLE IF NOT EXISTS meta.job_audit (
  run_id            VARCHAR,
  job_name          VARCHAR,
  status            VARCHAR,       -- success|failed
  start_time        TIMESTAMP,
  end_time          TIMESTAMP,
  src_row_count     BIGINT,
  tgt_row_count     BIGINT,
  job_version       VARCHAR,
  watermark_value   TIMESTAMP,
  error_message     VARCHAR
);

Drills and exercises

• Create a merge statement for a table with composite key (order_id, line_no). Verify idempotency by running it twice.
• Write a SQL window query to select the latest record per customer using row_number().
• Implement a retryable API pull with exponential backoff and a max attempt cap.
• Add an audit write step that logs row counts and duration for any pipeline you built.
• Parameterize schema names and file paths via environment variables for dev and prod.
• Quarantine an unexpected column by writing it to a separate table and notifying via logs.

Common mistakes and how to debug

Mini project: Daily product sales pipeline

Build a daily ETL that ingests sales files and enriches them with product data from an API, then publishes a clean fact table.

• Sources: CSV files in a landing folder; product details from a paginated API.
• Targets: staging.sales_raw, staging.products_raw, core.fact_sales.
• Requirements: idempotent upsert for products; incremental load for sales using a watermark; audit logging; environment configs.

Subskills in this skill

• Working With ETL Tools Basics — Create jobs, schedule runs, and inspect logs to understand execution flow.
• SQL Based Transformations — Write robust joins, window functions, and merges for idempotent loads.
• Scripting For Automation — Use Python or Bash for helpers: file moves, API calls, and parameter passing.
• Connecting To APIs And Files — Read/write CSV/JSON/Parquet; handle pagination, rate limits, and retries.
• Handling Schema Changes — Detect drift, default missing fields, and quarantine unexpected columns.
• Logging And Audit Tables — Record run metadata, counts, versions, and errors for observability.
• Environment Configuration Dev Stage Prod — Separate configs and secrets; promote safely across stages.
• Deployment Practices Basics — Version, test, migrate, and rollback ETL changes reliably.

Next steps

• Work through each subskill in order, building a single cohesive pipeline as you go.
• Add data quality checks: uniqueness, null thresholds, and referential integrity.
• Take the skill exam below to validate your readiness. Anyone can take it; logged-in users get saved progress.