Data Engineering: Aspects & Skills
Data engineering is the process of designing, building, testing, and maintaining the infrastructure that is necessary for an organization to effectively manage and analyze large amounts of data. This includes the creation of data pipelines, databases, and data warehouses that allow businesses to extract insights from their data.
Some of the key aspects of data engineering include:
1. Data ingestion/acquisition: Data engineers need to be able to extract data from various sources, including databases, APIs, and flat files.
2. Data transformation: Data often needs to be cleaned, transformed, and enriched before it can be analyzed. Data engineers need to have a deep understanding of data manipulation tools and techniques to ensure that data is properly formatted and cleaned.
3. Data storage: Data engineers must choose the appropriate storage technologies and data models to store data in a way that is efficient and scalable.
4. Data processing: Data engineers must design and implement processing pipelines that can handle large volumes of data in real-time.
5. Data governance: Data engineers must ensure that data is properly managed and secured to comply with regulations and to protect sensitive information.
The mechanism that automates ingestion, transformation, and serving steps of the data engineering process is known as a data pipeline. Constructing and maintaining data pipelines is the core responsibility of data engineers.
Some of the key technical skills required for data engineering include:
1. Programming: Data engineers must be proficient in at least one programming language, such as Python or Java, and be able to write efficient and maintainable code.
2. Data modeling: Data engineers must have a deep understanding of database design principles and be able to create efficient data models that can handle large volumes of data.
3. ETL (Extract, Transform, Load) tools: Data engineers need to be proficient in ETL tools such as Apache Airflow, Apache Spark, or Apache NiFi, which are used to move data from one system to another.
4. Big data technologies: Data engineers must be familiar with big data technologies such as Hadoop, Hive, and Impala, which are used to store and process large volumes of data.
5. Cloud technologies: Many organizations are moving their data infrastructure to the cloud, so data engineers must be familiar with cloud technologies such as Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP).
6. Data visualization: Data engineers must be able to create visualizations that help stakeholders understand the data and make informed decisions.
Overall, data engineering is a complex and ever-evolving field that requires a combination of technical and soft skills, including strong problem-solving abilities, attention to detail, and effective communication skills.
Some key terms:
Data Wrangling: Converting raw data into a usable format for analytics, BI, and machine learning projects (Steps: Data Cleaning, Transformation, Enrichment, Integration)
Data orchestration: The process of taking siloed data from multiple data storage locations, combining and organizing it, and making it available for data analysis tools. Data orchestration is the process of coordinating and automating the movement of data between different systems, applications, and processes. (Steps: Organise, Transform, Activate)
Online Analytical Processing(OLAP): Technique that allows users to perform complex, multi-dimensional analysis of large datasets. OLAP databases are designed for decision-making and analysis and typically store historical data. OLAP databases are optimized for read-heavy workloads and complex queries that require the aggregation of large amounts of data across multiple dimensions. OLAP databases typically use a star or snowflake schema and are structured in a way that allows for efficient and fast querying of data. Examples of OLAP tools include Microsoft Analysis Services, Oracle OLAP, and IBM Cognos
Online Transaction Processing (OLTP): On the other hand, is a data management approach that focuses on transaction processing, which involves the insertion, modification, and deletion of data in real-time. OLTP databases are designed for operational systems such as e-commerce platforms, bank transaction systems, or airline reservation systems, which require constant updates to data. OLTP databases are optimized for write-heavy workloads and simple queries that involve the retrieval of individual records. OLTP databases typically use a normalized schema and are structured to ensure data consistency and integrity. Examples of OLTP databases include MySQL, Oracle Database, and Microsoft SQL Server.
Data warehouse: Central repository storing data in queryable forms. From a technical standpoint, a data warehouse is a relational database optimized for reading, aggregating, and querying large volumes of data. Data warehouse is constructed to deal mainly with structured data for the purpose of self-service analytics and BI;
Data lake: Vast pool for saving data in its native, unprocessed form. It stands out for its high agility as it isn’t limited to a warehouse’s fixed configuration. Built to deal with sizable aggregates of both structured and unstructured data to support deep learning, machine learning, and AI in general
Data hub: Created for multi-structured data portability, easier exchange, and efficient processing.
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