What does data engineering mean in the context of big data?

Data engineering is the domain that formulates, designs and implements systems and pipelines that can efficiently converge, divide, and map out vast quantities of data. In this article, we will learn about data engineering in the context of big data.

What is Bigdata?

Big data is a large and complex data set, that exceeds the traditional ability of data management systems in terms of processing. It is often characterized by the "5 Vs":

• Volume: With the large volume of data obtained from many streams, including social media, sensors, and transactions,.
• Velocity: The high speed designed for data generation, processing, and analysis most times works in near Real-time, or Real-time.
• Variety: With the variety in data types and formats from structured, semi-structured, and unstructured data.
• Veracity: The type of the data and its reliability are the key factors, which include noise, biases, and data accuracy of data.
• Value: The possibility of teasing out relevant and useful insights from big data through their analysis to generate and make strategic decisions.

How data engineering is used to handle Bigdata?

1. Volume:
   • Scalable Storage Solutions: Data engineers develop high-performance data center products like data lakes, data warehouses, and distributed file systems to accommodate large data volumes.
   • Partitioning and Sharding: They implement partitioning or sharding methods to distribute data across multiple storage nodes for effective management and access.
2. Velocity:
   • Real-Time Data Processing: Data engineers use stream-oriented platforms such as Apache Kafka and Apache Flink for processing real-time data, enabling rapid data ingestion and processing.
   • Buffering and Queuing: They implement data buffering and queuing mechanisms to ensure smooth data flow and manage high-speed data streams effectively.
3. Variety:
   • Data Transformation: Employing an ETL (extract, transform, load) process, data engineers harmonize different data types into a single, structured format for analysis.
   • Schema Management: They oversee schema evolution and maintain data integrity between different databases to ensure consistency across multiple data formats.
4. Veracity:
   • Data Quality Assurance: Data engineers establish data validation, cleansing, and enrichment procedures to ensure data accuracy and credibility.
   • Metadata Management: They build data asset repositories and data catalogs to track data lineage and quality at the data pipeline level.
5. Value:
   • Data Pipeline Optimization: Data engineers optimize data pipelines to accelerate data intake for faster insights and analysis.
   • Collaboration with Data Scientists and Analysts: They work closely with data scientists and analysts to ensure that data sources are cleaned and formatted correctly, facilitating valuable insights and business outcomes.

By addressing these aspects, data engineers play a critical role in enabling organizations to effectively manage and derive insights from Big Data.

Tools and Technologies Used in Data Engineering used to handle Big Data

A comprehensive list of tools and technologies commonly used in various aspects of data engineering are as follows:

1. Data Storage and Management:
   • Hadoop: A distributed storage and processing framework using HDFS (Hadoop Distributed File System) for large data clusters.
   • NoSQL Databases: Solutions like MongoDB, Cassandra, and Couchbase manage semi-structured and unstructured data, offering scalability and flexibility.
   • Cloud Storage: Managed services like Amazon S3, Google Cloud Storage, and Azure Blob Storage provide scalable solutions for handling vast amounts of data.
2. Data Processing and Analytics:
   • Apache Spark: A fast data processing engine with in-memory computation, suitable for analytics and machine learning with support for batch and stream data.
   • Apache Flink: Streams data in real-time, enabling high-volume analytics for quick decision-making.
3. Data Ingestion and Streaming:
   • Apache Kafka: Centralized streaming ecosystem for transmitting huge data quantities in real-time.
   • Apache NiFi: An integration tool automating data exchange between different systems.
4. Data Orchestration and Workflow Management:
   • Apache Airflow: Orchestrates batch-processing pipelines and automates task execution for ETL (extract, transform, load).
   • Luigi: A Python-based automation platform by Spotify for automating data tasks.
5. Data Transformation and ETL:
   • AWS Glue: An AWS service streamlining data discovery, transformation, and loading steps.
   • Apache Beam: Offers a universal programming model for both batch and stream processing, enabling the development of application pipelines.
6. Data Quality and Governance:
   • Great Expectations: Open-source data validation and documentation framework maintaining high data quality standards along pipelines.
   • Collibra: Information governance framework managing metadata, data lineage, and data quality across the enterprise.
7. Monitoring and Logging:
   • Prometheus and Grafana: Monitoring and visualization tools for tracking data pipelines and system performance.
   • ELK Stack (Elasticsearch, Logstash, Kibana): Tools for logging, searching, and visualization, suitable for application monitoring and data troubleshooting.

In conclusion, data engineering as an integral part of the big data revolution lies at the heart of driving robust data infrastructure that provides insights and empowers decisions. Data engineers can achieve the desired level of quality and performance by implementing the best practices i.e. helping it to scale, guaranteeing data availability, application of automation features, and resilience to security breaches.