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:
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
- 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.
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.
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