What are some common data engineering project types?

No contexto da engenharia de dados um data warehouse é um dos projetos mais significativos pois é um repositório central de informa??es que irá permitir vários insigths estratégicos para a organiza??o. área de business intelligence (BI), ciência de dados e de negócios s?o alguns consumidores desses dados que podem utilizar vários Data Warehouse de mercado como alguns citados abaixo: - Amazon Redshift - Microsoft Azure Synapse Analytics (Pool de Sql Dedicado - Antigo SQL DW) - Google BigQuery - Snowflake

In the realm of data engineering, the construction of a data warehouse is a foundational project type that serves as the backbone for decision-making processes. My experience has shown that a well-designed data warehouse not only facilitates efficient business intelligence and analytics but also enhances collaborative efforts across different departments. The ETL processes are critical; they must be robust and scalable to handle the ever-increasing volumes of data. In my projects, leveraging cloud technologies like Azure and Snowflake has provided the flexibility and scalability needed to support dynamic business requirements.

A data warehouse is a single location that stores a single version of truth (i.e. the most accurate, reliable and complete version of enterprise data). It's usually made up of multiple data sources (3rd party apps, files, databases, APIs, etc) and can process information in a structured format and upload them to targeted locations stakeholders want them in. Most traditional data warehouses stored transformed data into physical servers and databases, and modern data warehouses store enterprise data into storage units in the cloud (i.e. data lakes).

Some common data engineering project types include: 1. Data Warehousing: Designing and building data warehouses to store, manage, and analyze large volumes of data from various sources. 2. Data Integration: Developing systems to consolidate and integrate data from multiple sources into a unified view for analysis and reporting. 3. Data Pipelines: Creating ETL (extract, transform, load) or ELT (extract, load, transform) pipelines to move and process data from source systems to target data warehouses or data lakes. 4. Real-time Data Processing: Building systems to handle streaming data and process it in real time for analytics, alerts, or other applications. 5. Data Quality and Governance

Define a data Warehouse process can be challenges for following points: ?? What willl data modeling (data vault, dimensional, anchor, etc) use to organize all data? ?? What will components use for specific lifecycle phase? ?? How will it be integrated with data mart, ODS or data lake to work? ?? How will it support topics like data deletion request or data subject request access? ?? Will it support batch or streaming ingestion of data? ?? What data storage will choose to implement specific component?

Data lakes are foundational in handling the variety, velocity, and volume of data in today's fast-growing data landscapes. As a data engineer, I've seen their evolution from mere storage repositories to sophisticated ecosystems with advanced analytics capabilities. By leveraging distributed file systems like Hadoop and cloud services such as Amazon S3, we can build scalable solutions that not only store vast amounts of raw data but also provide a flexible platform for data exploration and advanced analytics. Implementing proper data governance and cataloging is crucial to ensure that data lakes remain manageable and valuable to the organization.

Os Datalakes ajudam a disseminar a cultura data driven nas organiza??es e isso acontece de várias maneiras. Ele permite armazenar grandes quantidades de dados em um único local, o que facilita o acesso e a análise desses dados por diferentes equipes e departamentos. Além disso, o Datalake pode ser usado para armazenar dados brutos, sem a necessidade de estruturá-los previamente, o que permite que os usuários possam explorar e analisar os dados de maneira mais livre e flexível.

Data lakes are storage units that can save large amounts of data in different formats (CSV, JSON, XML, TXT etc). So... why do we need them? 1. They can store a broader range of useful data types than traditional storage systems 2. it's easier to scale them when a company's data rapidly grows into large quantities i.e. it's more cost effective to scale a data lake than traditional databases and data warehouses (in terms of time, money, efforts). 3. Data lakes use a "schema-on-read" approach instead of "schema-on-write" (which is what databases normally use). "Schema-on-read" means the system reads and stores the data in its raw form. "Schema-on-write" means the system defines the data structure first and then reads it.

Projects involving Data Lakes focus on ingesting diverse data types without prior structuring, enabling raw data storage for various purposes like analytics, machine learning, and exploration. These projects often involve implementing data ingestion pipelines, metadata management, and access control mechanisms. Tools like Azure Data Lake Storage and Azure Databricks are commonly used in these projects to manage, process, and analyze vast amounts of data in its native format.

In the rapidly evolving field of data engineering, constructing robust data pipelines is crucial for ensuring consistent and efficient data flow. My experience has shown that tools like Apache Airflow are indispensable for orchestrating complex workflows, while Apache Spark excels in processing large datasets efficiently. Moreover, Apache Kafka is a game-changer for real-time data streaming, enabling collaborative and responsive data-driven decision-making. Balancing the needs of batch and real-time processing within pipelines is key to supporting diverse analytics platforms and ensuring data is actionable when it matters most.

Tabular data are the most common data in the pipelines you will meet in the industry. This data can be stored in files (parquet, avro, ocr, csv, json, iceberg) or in sql based databases (bigquery, snowflake, postgresql). According to Fundamentals of Data Engineering (O'Reilly editions), files were mostly used in the past, forming vast data lakes. But files are getting more and more replaced by sql based databases. The consequence is that popular hadoop clusters using spark, might be on the decline. On the other hand, sql based pipelines (airflow, dbt, templated sql queries) are the rise. What is interesting to note about dbt is that an O'reilly book has been published about the technology in 2024.

A data pipeline is an algorithm that extracts, transforms and loads data between different locations. These locations (both source and destinations) can include - databases - data lakes - reporting tools - 3rd party apps - CRMs + ERPs - REST APIs You can build data pipelines using - SQL - Python - Spark - Kafka - Flink - Airflow/Prefect/Mage - Databricks

Data pipelines are frameworks that facilitate the automated flow and transformation of data from one point to another. Projects involving data pipelines aim to streamline the movement of data across different systems, ensuring efficiency, reliability, and scalability. They often encompass tasks like data extraction, transformation, and loading (ETL), real-time streaming, or batch processing. In Azure, tools like Azure Data Factory play a pivotal role in constructing these pipelines, orchestrating the movement of data between various sources and destinations, enabling seamless data integration and processing.

Building and maintaining data pipelines is crucial. We rely on tools such as Apache Airflow, Apache Spark, Databricks, SQL and SSIS to ensure a flow of data from sources to warehouses or analytics platforms. It's like being a plumber for data dealing with formats and orchestrating real time processing like a symphony. Troubleshooting adds a fulfilling aspect to the job. With Apache Airflows scheduling and monitoring capabilities and our exploration of Apache Spark we enhance our ability to handle large scale data processing.It involves navigating through datasets utilizing tools and embracing challenges in order to establish a strong foundation, for our data infrastructure.

Define a data integration process can be challenges for following points: ?? How tool will use to be scalable in workload while keep efficient cost as possible. ?? Can use metadata to detect possible problems in process? ?? How will self-healing help to continue with the process? ?? What will tool like data federation or virtualization can accelerate the development and maintenance process?

Data integration projects focus on merging data from different sources into a unified, coherent view. This involves combining data from disparate systems, databases, or applications to create a consolidated and accurate dataset. These projects often encompass tasks like data cleansing, transformation, and normalization to ensure consistency and quality. Tools like Azure Data Factory or Azure Synapse facilitate these projects by providing capabilities to connect, transform, and integrate data from various sources into a single, harmonized format, enabling better analytics, reporting, and decision-making.

Data quality projects aim to ensure the accuracy, consistency, and reliability of data. These projects involve assessing, improving, and maintaining the quality of datasets by identifying and rectifying issues like duplicates, inconsistencies, missing values, or errors. Activities include data profiling, cleansing, standardization, and establishing data quality metrics. Azure Data Factory and Azure Data Quality Services are tools commonly used in these projects to implement data quality checks, validation rules, and monitoring mechanisms, ensuring that the data meets the required standards for reliable analysis and decision-making.

Data is a representation of a real world event. Measuring data quality means we check - how consistent it is across different sources (consistency) - how well it represents events that actually happened/is happening (accuracy) - if all necessary data is present (completeness) - if the content is dependable and trustworthy (reliable) - if data is up to date (timelessness) High-quality data ticks all of these well, and a low-quality datasets falls short in these aspects i.e. contains inaccurate numbers, corrupt characters, misspellings, missing data etc. We run data quality tests to check if the actual data generated matches the data expected. Here are some Python libraries for DQ tests: - pytest - pydantic - great_expectations

A data engineering project related to data science might be to make raw data available for data scientists. It is common for data scientists to request data closer to the "raw" layer in one wide table so they can explore the data, apply their own transformations, and create the features they need.

Understand what your internal customers want. Your internal customers are the business units that send you feature tickets and requests, or assign projects your way. Sit down with them and let them clearly articulate what they want from you. - What's their goal? - What data do they need to achieve this? - What tools do you need to bring this to life? - What should the data and its tests look like? - What should/shouldn't it contain? - Where do they want it once we've done our part? - How frequently do they want it? - How often should we update it? - How do we prioritize feedback?

In data engineering consulting, data warehouse migration is a common type of data engineering project. Clients have a data warehouse (or database) on a legacy platform like Oracle or on-prem SQL Server, and they want to migrate to a more modern platform like Snowflake, Databricks, or BigQuery. Typically, this is driven by the legacy platform not meeting the client's performance requirements. Some tasks in a data warehouse migration might include: - Investigation of the existing Warehouse - Architecting the new warehouse - Migration and re-working of the existing ETL pipelines - Reworking reports to use the data in the new warehouse