Introduction When an organization needs to report on an event, extract operational insights through data analysis, or train a large language model, the common denominator is reliably consuming data. Data engineering not only offers mechanisms to capture, store, process, and access data, but also tools to design and optimize data systems that enable decisions and products such as custom applications and custom software solutions.
1. Batch ingestion vs. streaming ingestion Batch ingestion groups data over a period and processes it all at once, for example hourly or daily loads, useful for end-of-shift reports. Streaming ingestion processes each record as it arrives, ideal for real-time metrics or Power BI dashboards showing instant sales.
2. Change Data Capture (CDC) Change data capture detects insertions, updates, and deletions at the source and applies only the changes downstream without reprocessing the entire dataset. Without CDC, the full table would be exported periodically, generating high costs and duplicates. With CDC, latency and resource usage are reduced. Common tools include Debezium, Oracle GoldenGate, and AWS Data Migration Service.
3. Idempotency Idempotency guarantees that running the same operation multiple times, for example restarting an ingestion job after a failure, has the same effect as running it once, avoiding duplicates. Common techniques: upserts, unique keys, and identifier-based deduplication.
4. OLTP vs. OLAP OLTP is oriented toward fast, consistent, and concurrent transactions for operational systems that perform many small operations. OLAP is designed for aggregations, trend analysis, and multidimensional queries over large volumes of data, typical in business intelligence services and advanced dashboards.
5. Partitioning Partitioning divides large datasets into manageable portions based on a key such as date or region to improve query performance and administration. Common types: range partitioning, list partitioning, hash partitioning, and composite partitioning which combines strategies for fine control.
6. ETL vs. ELT ETL extracts, transforms, and loads the transformed data to the destination. ELT extracts, loads first, and transforms in the destination system, leveraging the storage and processing power of the data warehouse or lakehouse to reduce data movement.
7. CAP Theorem In distributed systems, only two of three properties can be guaranteed: consistency, availability, and partition tolerance. For example, Cassandra prioritizes availability and partition tolerance, while traditional SQL databases prioritize consistency and availability.
8. Streaming windows Streaming is continuous, so windows are used to group data into finite segments such as the last 5 minutes. Types: tumbling windows of fixed, non-overlapping size; sliding windows that overlap for rolling metrics; and session windows that group events by inactivity.
9. DAGs and workflow orchestration A DAG is a directed acyclic graph that orders tasks according to dependencies without cycles. Orchestrators like Apache Airflow or Prefect use DAGs to define, schedule, and monitor pipelines, coordinate retries, and manage backfills.
10. Retry logic and dead letter queues Retry logic reprocesses temporary failures that usually resolve when trying again. Dead letter queues store events that fail persistently for manual inspection. For example, a Kafka consumer can retry three times before sending the event to a queue for review.
11. Backfilling and reprocessing Backfilling ingests historical data that was missing due to outages or because a pipeline is new and must populate past data. Reprocessing applies updated logic to historical data to correct errors or adapt to schema changes.
12. Data governance Governance establishes rules, procedures, and best practices to maintain data quality, security, and compliance. This includes access control, cataloging, metadata management, and retention policies, critical aspects for artificial intelligence projects and regulatory compliance.
13. Time travel and data versioning Time travel and versioning features in technologies like Snowflake, Delta Lake, or Apache Iceberg allow querying historical states of the dataset for auditing, debugging, and recovery. Benefits: facilitates audits, allows comparing states for debugging, and supports safe experimentation.
14. Distributed processing concepts Distributed processing spreads the load across machines to scale. Key concepts: sharding to divide data among nodes, replication for fault tolerance, and MapReduce to map tasks and reduce aggregated results.
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How we can help We design ETL and ELT pipelines, implement CDC, partitioning, and time travel on modern platforms, orchestrate workflows with DAGs, and deploy artificial intelligence models and AI agents in production environments. Typical integrations include custom solutions, Power BI dashboards, deployments on AWS and Azure cloud services, and robust cybersecurity strategies.
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