Data Engineering
Data engineering is basically an engineering of system and architectures for collecting, storing, analyzing data at scale. Data engineers are those who do it. They ensures that the data is readily available, reliable, and useable for data analysis and decision-making. The core components of data engineering are:
Data collection:
Data may need to be gathered from various databases, APIs, logs, and external sources. Also, data may need to be collected in real-time such as streaming data called real-time processing or in chunks at a regular intervals called batch-processing.
Data Modeling
Data modeling is the foundational blueprint for how the data will be structured, stored and related within the database or data warehouse. Different guidelines, entities, relationships, schemas are developed for clear framework for data storage and access. E-R diagram, data normalization can be techniques used for data modeling. Snowflake Schema, Microsoft Visio are some tools which can be used for this.
Data storage:
Collected data needs to be stored for further processing and using it to draw necessary insights. Data can be stored in various ways. Some of them are:
Databases: Maybe relational(eg: MySQL, PostgreSQL) or NoSQL(eg: MongoDB, Cassandra)
DataLakes: Used to store large volumes of data in native format(eg: Hadoop, Amazon S3)
Data Warehouses: Used to store processed and structured data for analysis(eg: Snowflake, Amazon Redshift)
Data Integration:
It is the process of combining data from different sources to provide a unified view of the data which is very important for accurate analysis and reporting.
For example, a company can collect its customers data from its CRM, sales data from its e-commerce terminal and marketing data from Google Analytics. Then, the data will be transformed into common format like merging same customer records across all platforms and loading it to central data warehouse. It can be done as batch integration as well as real-time integration.
Apache Kafka for real-time data-pipeline and Apache Spark for large-scale data processing can be used.
Data Processing:
Data processing is the process of converting collected data into useful information. There are certain techniques very important to understand.
ETL(Extract, Transform, Load): It involves three processes, extracting data from various sources and collecting in a single repository, transforming data in a uniform format, cleaning, mapping, augmenting it to meet the organizational requirements, and finally loading the data for sharing securely for use by others.
Data Pipelines: It deals with automating the flow of data from source to destination. It involves all steps from data ingestion(collection) to transformation, validation and loading both real-time and batch data.
Distributed Computing: It is very important in context of handling big data processing efficiently. It concerns with dividing large datasets and computational tasks across multiple machines thus allows for parallel processing, fault tolerance, and scalability.
Data Quality and Governance:
Data quality is very important for the correct analysis and extracting true insights.
Thus, identifying and removing inconsistencies in data is crucial. Data must meet certain standards and criteria before being stored. Data engineers keep track of data origins, transformations, and usage to ensure data compliance.
Data Security:
Data security is very important part of data engineering. Data engineers manage who can have access to the data and manipulate them. They focus on protecting data at rest or at transit from unauthorized access using techniques like encryption. They ensure the data handling practices meet legal and regulatory requirements as well.
Big Data Technologies:
Big Data refers to the large and complex datasets that traditional data processing tools cannot handle and process efficiently.
For handling big data following technologies can be used:
Hadoop: It is an open-source framework for distributed storage and processing of large data sets. It specifically uses MapReduce programming model (distributed algorithm for big data).
Spark: It is fast and general purpose cluster computing model (use of cluster of computers as a single system) for big data. The biggest advantage spark possess is its in-memory processing capabilities.Data Pipelines:
Data Pipelines automate the flow of data from sources to destinations. It involves steps such as steps for extraction, transformation and loading.
Pipelines should be designed in modular fashion, should be timely monitored and in a way that they should be able to handle increased data volumes. Apache Airflow is the most popular tool for authoring, scheduling and monitoring pipelines.
Other very important concept a data engineer should know about is databases (SQL and NoSQL), query optimization and indexing techniques (for faster retrieval of data), and now cloud data engineering (such as data engineering in AWS Glue, Google Dataflow for better scalability, cost-efficiency and flexibility).
Best Practices in data engineering
For better reliability, efficiency, easiness and data quality, one should go for best practices in data engineering. Some of them are:
Using version control for data pipelines:
One should use version control systems like Git to manage changes and tracking history of data pipelines. It helps in collaboration between team members, rollback to previous versions and auditing of changes made.
Monitoring and logging:
Robust monitoring and logging is very very important in maintaining health of data pipelines. They are very crucial for detecting issues early, diagnosing problems, and ensuring accuracy and timeliness.
Tools like Datadog, Graylog, Grafana, Prometheus, Perfect UI have their own usecases in monitoring and logging such as end-to-end monitoring, log analysis, real-time monitoring and so on.
Testing and Validation:
Testing can be done for pipelines built using Python with PyTest for unit tests. Automated testing can be done using CI/CD pipeline on Jenkins. Simulations can be done to validate the entire workflow.
Current Trends in Data Engineering
Real-time Data Processing:
The need of real-time data processing and analysis is increasing rapidly. It is very essential for companies to make fast data-driven decisions, response with immediate insights, and enhance user experiences. Real-time processing deals with streaming data. It plays very critical sectors like e-commerce, banking, telecommunications and so on. Apache Kafka is very popular platform for reading (subscribe), writing (publishing), storing and processing streams of data in real-time. Some other tools are Apache Flink, Apache Pulsar.
DataOps:
DataOps is basically same as concept of DevOps but in the field of data management. It is an emerging concept which focuses on improving the integration and collaboration between data engineers, data scientists, and operations teams. DataKitchen and Apache Airflow are widely used for data pipelines and facilitating complex data workflows, improving collaboration.
Machine Learning Integration:
Machine Learning has been a hot-topic since ages for now. Integrating data engineering with ML workflows is very crucial for building scalable and efficient ML applications. It can be highly beneficial in aspects from feature engineering, model training and evaluation to model deployment. I personally love MLflow for managing ML lifecycle from experimentation to deployment. Other tools for this are very popular Apache Spark MLlib, Tensorflow Extended, and so on.
Automation and Orchestration:
Automation and Orchestration is heart of data pipelines and seamless workflows. They significantly reduce human intervention, minimize errors and improve scalability. Some current practices includes automated data pipelines for automating workflows to ETL data from sources into targeted storage systems.
Also orchestration is currently used widely to handle dependencies, schedule tasks to optimize resources usage and ensure timely data processing.
Future Trends in Data Engineering
Serverless Data Engineering:
Serverless architecture will benefit data engineers by letting them focus only on engineering tasks without managing the servers. Cloud providers will automatically allocate resources and scale them as needed. It will be more scalable, cost-effective, simple, and event-driven uplifting real-time processing.
Some examples are AWS Lambda, Azure Functions which runs code in response to events and automatically allocate the resources needed.
Edge Computing:
Edge computing is quite fascinating term but its nothing more than processing data near the source where it is generated (eg: IoT sensors) rather than sending data to the centralized data centers.
Data Mesh:
DataMesh is a decentralized approach to data architectures and management. In this approach, data is treated as product and owned by domain-specific teams.
This impacts in delegation or simply self-responsible team for their own data products. It helps to ensure consistent data standards and practices.
AI and ML driven Data Engineering:
Data engineering is one of the application field of AI. How cool is it to optimize and automate the data engineering process without needing human intervention!!
Automated data cleaning, predictive maintenance, AI-driven optimization of ETL workflows, data anomaly detection are just crazy.Some tools that can be used are DataRobot, Apache Spark MLlib, H20.ai which provides automation for data science workflows.
Graph Databases:
Graph Databases are very useful for handling data with complex relationships and interconnections, represented as nodes and edges. The major advantages of graph databases are that they are very optimized for querying and analyzing data with complex relationships such as social networks or recommendation system, they are very flexible and performance is very efficient for traversing and querying data graph structure.
Some examples of graph databases are Neo4j, Amazon Neptune, and so one.
Conclusion
As the field of data engineering is widely and rapidly evolving, it is very important to understand its core and focus on the new coming trends and technologies. By navigating through the evolving landscape of data engineering, data engineers can overcome challenges and leverage opportunities to drive business value and innovation through data. Data engineering can be the steering to the organizational success if able to harness the full potential of their data assets.