You’re a data engineer. What are the most important pipeline techniques you need to learn?

Data validation is crucial for data engineers. First, data profiling helps assess the quality of new data sources. It analyzes data for completeness, accuracy, and patterns, uncovering potential issues early on. Second, data observability is key for detecting anomalies. It monitors data for unexpected changes or deviations. Observability platforms often run separately from the pipeline, offering a wider safety net for quality issues. Finally, traditional data quality checks are essential. These include pattern matching for correct formats and validating individual records. The data pipeline can integrate with a data quality platform to run these checks, ensuring a streamlined and comprehensive quality control process.

I agree that all aspects of data engineering are important however I think data validation takes preference since if the data is not up to the mark it will generate incorrect KPIs and also the estimates and forecasts will not be accurate

All aspects of sharing data for business insight and downstream systems are important. Each component of the data pipeline plays a crucial role in ensuring the smooth flow of data and enabling valuable insights. From extraction to transformation, loading, integration, quality, workflow orchestration, and governance, every step is essential for achieving accurate and meaningful business insights. Each aspect contributes to the overall effectiveness and efficiency of the data sharing process. So, it's important to give equal attention and importance to all components of the data pipeline. ETL Batch Processing Real-time Streaming Data Integration Data Quality Workflow Orchestration Data Governance

While building a data pipelines,it is quite essential to have robust data security in place with proper encryptions.Data security is essential to ensure data is classified according to its sensitivity and defining processes for safeguarding critical information and preventing data loss and leakage.Apart from that Data integrity should be maintained, ensuring data retains its essential qualities even as it is stored, converted, transferred and viewed across different platforms.

A valida??o de dados é essencial; sem ela, estamos basicamente navegando às cegas. Imagine que estamos construindo uma ponte. A valida??o dos dados seria como inspecionar cada tijolo, cada viga de a?o, antes de adicioná-los à constru??o. N?o queremos descobrir depois que a ponte está pronta que um dos tijolos estava quebrado, certo? Isso poderia comprometer toda a estrutura. A valida??o de esquema é uma das primeiras coisas que você aprende. é como verificar se o tijolo que você tem é do tamanho e formato certos para a ponte que está construindo. Basicamente, você garante que os dados estejam no formato esperado antes de prosseguir. Se estamos esperando números e recebemos texto, algo está errado.

As a data engineer, mastering data partitioning techniques is crucial for efficient data pipelines. Key techniques include range partitioning, hash partitioning, and list partitioning, each offering unique advantages based on data distribution and access patterns. Understanding how to partition data optimally ensures improved query performance, scalability, and resource utilization in distributed systems, enhancing overall data processing efficiency and reliability.

Effective partitioning strategies can significantly enhance query performance and data management. Partitioning data by certain keys or time frames enables more efficient data retrieval and can optimize storage, especially in distributed systems.

Para bigdata é fundamental particionar os dados usando um bom critério, na maioria das vezes usamos data, porém é necessário entender o que será consultado, pois se sempre é consultado a base histórica, por exemplo, n?o vale a pena particionar por data. Por tanto, é importante entender um pouco sobre como ser?o usados os dados para que possa particionar de maneira assertiva.

Como engenheiro de dados, dominar as técnicas de particionamento de dados é crucial para pipelines de dados eficientes. Técnicas-chave incluem particionamento por faixa, particionamento por hash e particionamento por lista, cada um oferecendo vantagens únicas com base na distribui??o de dados e padr?es de acesso. Compreender como particionar os dados de forma otimizada garante melhor desempenho de consulta, escalabilidade e utiliza??o de recursos em sistemas distribuídos, aprimorando a eficiência geral de processamento de dados e confiabilidade.

Data partitioning improves the performance, scalability & manageability. Horizontal Partitioning divides large dataset based on rows/records into multiple databases/tables. Vertical divides the dataset based on columns/attributes when different columns are accessed by different queries & frequencies. Key-Based uses primary key to store related data together used in distributed systems. In Range, data is partitioned based on date ranges where queries often request a specific time frame. In Hash-Based data is distributed based on the hash value of a partitioning key to evenly distribute data across partitions to optimize query performance. Round-Robin distributes data evenly across all available partitions & not consider value of any data.

é fundamental dominar os conceitos de ETL ou ELT em um pipeline de dados, pois indicam a maneira de orquestrar o processo para obter dados utilizáveis pelas equipes de negócios. Dependendo da ferramenta de orquestra??o escolhida, você pode alcan?ar eficiência, integra??o de desempenho com outras ferramentas ou robustez no tratamento de erros em seu pipeline de dados.

Use data orchestration to define the data workflows, sources, destinations, transformations, & conditions understanding the data lifecycle. Automate the data pipeline using orchestration tools like Apache Airflow/Jenkins for scheduling tasks, managing dependencies. Implement robust error handling mechanisms including retries, exception handling, and error notifications. Continuously monitor the health and performance of the data pipeline. Use logging to track the pipeline’s activities and performance metrics. Efficiently manage compute power and memory to ensure pipelines run optimally. Incorporate data quality checks to ensure that the data is accurate, consistent, & reliable. Break the pipeline into small, reusable tasks.

I think that is a basic point you must domain it because all data pipeline must need to organize their process to work. ETL or ELT approaches in data pipeline indicate a way to orchest your process to get data to use from business teams! Depend of orchestration tool, you can get efficient, performance integration with another tools or robustness in error handling in your data pipeline.

**Data Orchestration**: The process of coordinating and managing data workflows, ensuring tasks are executed in the correct order and dependencies are met for efficient data processing.

Picture data orchestration as conducting a symphony of data movements and transformations, coordinating each component seamlessly to produce harmonious results. Familiarize yourself with orchestration tools and frameworks that automate and manage complex data workflows. Streamline pipeline execution, scheduling, dependency management, and error handling to ensure smooth and reliable data processing.

A linhagem de dados é uma das técnicas mais fundamentais que você precisa entender em seu ambiente de dados. Quando algo dá errado com seu pipeline de dados, é o primeiro lugar para onde você irá para solucionar problemas e entender o fluxo de dados. Existem várias ferramentas de código aberto e também pagas disponíveis; certifique-se de avaliá-las antes de tentar construir a sua própria.

Data Lineage tracks and documents the origin, transformation, & destination of the data that flows through pipeline. Parsing-Based parses the underlying code to understand the flow and changes. Tagging based tags data as it moves between systems for audit trails & compliance. Pattern-Based analyses metadata & data shapes to match patterns. Visualization Tools utilize data lineage to create a graphical representation of the data flow. Metadata Management to capture data origin, transformations, & final destinations. Forward & Backward Lineage tracking the flow of data from source to destination & backward lineage tracing back to the data’s origin. Impact Analysis on how to conduct impact analysis using data lineage information.

Having easily accessible, complete, and frequently updated lineage cuts troubleshooting times down drastically. Being able to quickly see the output of previous transformations allows you to pinpoint the issue quickly without having to search through poor documentation and having to reach out to multiple people or teams. Make updating your lineage part of your process if you don't have a tool that automates it for you!

Data lineage is one of the most fundamental techniques you need to understand your data environment. When something goes wrong with your data pipeline, it's the first place you will go to troubleshoot it and understand the data flow, there are several open source tools out there and also paid ones, make sure you evaluate them before trying to build our own.

**Data Lineage**: Tracking the origin and movement of data throughout its lifecycle, essential for understanding data quality, compliance, and impact analysis.

Data Modeling is one of the most important pipeline techniques to learn as it is the foundation for arranging/structuring the data which simplifies storage, retrieval and analysis. Data Modeling is crucial in 1. Understanding the data 2. Ensuring data integrity by enforcing constrains, validations based on business rules/requirements 3. Making better decisions Simple example: You cannot really model your data for a OLTP and perform OLAP on top of the data model. If you try to do so, you end up joining several tables as Data Modeling for OLTP is done with most normalized methods with less to no redundancy.

Data modeling is essential for structuring and organizing data in a way that aligns with business needs. It involves defining entities, relationships, and selecting the most appropriate data model. Careful consideration of normalization and denormalization is vital to maintain data integrity. Ensuring the inclusion of security and privacy aspects, like access controls and encryption, within the data model becomes imperative to safeguard sensitive information.

Modelagem de dados é a técnica de definir a estrutura, relacionamentos e restri??es dos dados armazenados e consumidos pelo seu pipeline. Isso ajuda a projetar e otimizar o esquema, formato e layout dos seus dados. Você pode usar várias ferramentas e linguagens para criar e gerenciar modelos de dados, como SQL, NoSQL ou JSON. A modelagem de dados pode ajudar a melhorar a usabilidade, legibilidade e compatibilidade do seu pipeline de dados. Também pode permitir que você suporte diferentes casos de uso, como analítico, operacional ou em tempo real.

Conceptual data modeling to understand the high-level structure of the data & its relationships by creating ER diagrams. Logical Data Modeling translates the conceptual model into a logical structure that defines the data elements & their relationships. Physical Data Modeling designs the actual structure of the database/tables/columns/indexes/views. Normalization to organize data in the DB properly. Dimensional Modeling for analytical processes of fact/dimension tables & star/snowflake schema designs. Data Warehouse design to consolidate data from multiple sources for reporting & analysis. ETL Design to efficiently move & transform data from operational systems to the data warehouse. Data Governance to understand the policies & practices.

View data modeling as sculpting raw data into a refined masterpiece, shaping it into structured formats that support analysis and decision-making. Develop proficiency in data modeling techniques such as dimensional modeling, entity-relationship modeling, and schema design. Design data models that align with business requirements, optimize query performance, and ensure data consistency and integrity throughout the pipeline.

Pipeline Optimization. The need to optimize existing pipelines comes up quite often as the size of data grows. Understanding the strategies to optimize your data pipelines is key to orchestrating reliable data pipelines.

Data catalougging and maintaining metadata of data is most important aspect that is often not considered / given lower priority in the overall scheme of things. In the data engineering, maintaining metadata about the data objects, models, reports, pipelines and lineage in a searchable format for an user is ultimate nirvana. There are many data catalogue and pipeline solutions available in the market that support the search capabilities.

In addition to data validation, partitioning, orchestration, lineage, and modeling, data engineers should also focus on mastering other important pipeline techniques. This includes error handling to ensure data quality and reliability, metadata management for tracking data throughout its lifecycle, incremental processing for efficiency, and data serialization for optimized transfer and storage. Understanding data compression, encryption, deduplication, and masking/anonymization techniques further enhances pipeline performance, security, and compliance. By mastering these techniques, data engineers can build robust, efficient, and secure data pipelines that meet the needs of modern data-driven organizations.

As you mentioned data lineage you should also say about data impact, which is reversal thing. If you change anything at the very beginning or at some intermediate step of your pipeline - you should know how your changes would impact data at the end.