Building the bases to enable data-driven products
Data Engineering
The ability of data engineering lies in its capacity to design and develop efficient and scalable data pipelines that can collect, store, process, and analyze large volumes of data from multiple sources. By implementing best practices for data integration, data quality, and data processing, data engineering can enable organizations to derive meaningful insights from their data, make informed business decisions, and gain a competitive advantage.
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What is it?
Understanding data engineering
Data engineering is the process of designing, building, and maintaining the infrastructure and tools needed to support the collection, storage, processing, and analysis of large volumes of data. Data engineers are responsible for building and managing the pipelines that enable data scientists and analysts to extract insights and value from the data.
What can you do?
Use cases of data engineering
Data science and AI
It involves designing, building, and maintaining the infrastructure and tools required for data acquisition, storage, processing, and analysis.
Machine learning
When working with machine learning (ML), data engineering enables the training and deployment of ML models on large datasets.
Data warehousing
Data engineering can be used to design and build data warehouses, which are central repositories for storing and managing large volumes of data.
Data governance
To ensure that data is collected, stored, and processed in compliance with legal and regulatory requirements data engineering must be used.
Business intelligence
Data engineering can be used to build data pipelines that enable business intelligence (BI) tools to extract insights and value from large volumes of data.
Internet of Things (IoT)
The collection and processing data from IoT devices, such as sensors and smart devices is a process that should take into consideration data engineering for better results.
Applications
E-commerce
E-commerce applications manages large volumes of transactional data, such as user activity, product sales, and inventory management. This enables businesses to optimize their sales and marketing strategies and improve their customer experience.
Healthcare
Data engineering is used in healthcare applications to manage patient data, such as medical records, lab results, and imaging data. This enables healthcare providers to improve patient care and outcomes by providing better diagnosis and treatment.
Financial services
Financial services applications manage large volumes of financial data, such as trading data, customer transactions, and risk management. This enables financial institutions to make better decisions and manage risks more effectively.
Marketing
Marketing applications handles and analyzes large volumes of customer data, such as demographics, behavior, and preferences. This enables businesses to improve their marketing campaigns and personalize their messaging to better target their customers.
Manufacturing
Manufacturing applications controls production data, such as machine logs, quality metrics, and supply chain data. This enables manufacturers to optimize their operations and reduce costs by improving efficiency and reducing waste.
Social Media
Social media analyzes large volumes of user-generated content, such as posts, comments, and likes. This enables social media companies to personalize their user experience and improve their content recommendations.
Data engineering in your organization
Production ready applications
Data engineering in production is critical for all types of organizations, the ones that need to handle large volumes of data efficiently and also the ones that want to give an structure to their data. This involves building and maintaining data pipelines that enable the continuous processing and analysis of data in real-time or near real-time.
These pipelines must be reliable, scalable, and efficient, ensuring that the data is processed and analyzed in a timely and accurate manner. By leveraging data engineering in production, organizations can gain valuable insights from their data, make data-driven decisions, and improve business outcomes.
Should I use this?
Data engineering is essential when you need to work with large volumes of data, perform complex data transformations, and create data pipelines that support real-time or near-real-time data processing.
However, it is important to note that data engineering is a complex and ongoing process that requires significant investment in terms of time, resources, and expertise.
Overall, data engineering can benefit your business by providing you with the necessary tools and infrastructure to manage and process large volumes of data efficiently.
What do I need to use them?
You may need to use data engineering when:
- You have large volumes of data: If your organization generates or collects large volumes of data, you may need data engineering to process, store, and manage it effectively.
- You need real-time data processing: If you need to process data in real-time or near real-time, you may need data engineering to build data pipelines that can handle the velocity and variety of data sources and process data as soon as it's generated.
- You need to scale your data infrastructure: If your organization is growing rapidly and generating more and more data, you may need data engineering to build scalable data infrastructure.
149
zettabytes of data are expected to be generated by 2024
Data
The volume of data needed for data engineering can vary depending on the specific use case and the type of data being processed. In general, data engineering is most commonly used when dealing with large volumes of data that are difficult to manage and process using traditional data management and processing techniques.
The exact threshold for what constitutes a "large volume" of data can vary depending on factors such as the type of data being processed, the complexity of the data, and the infrastructure being used. For example, a dataset that is considered "large" for one organization may be considered small for another.
However, as a rough guideline, data engineering is often used when dealing with datasets that are measured in terabytes (TB) or petabytes (PB) in size. For example, organizations that deal with large-scale data processing such as social media platforms and financial institutions.
Data
The volume of data needed for data engineering can vary depending on the specific use case and the type of data being processed. In general, data engineering is most commonly used when dealing with large volumes of data that are difficult to manage and process using traditional data management and processing techniques.
The exact threshold for what constitutes a "large volume" of data can vary depending on factors such as the type of data being processed, the complexity of the data, and the infrastructure being used. For example, a dataset that is considered "large" for one organization may be considered small for another.
However, as a rough guideline, data engineering is often used when dealing with datasets that are measured in terabytes (TB) or petabytes (PB) in size. For example, organizations that deal with large-scale data processing such as social media platforms and financial institutions.
149
zettabytes of data are expected to be generated by 2024
Popular data engineering services
What services can I use?
Amazon Web Services (AWS)
Offers a wide range of data engineering services, including Amazon EMR (Elastic MapReduce) for distributed processing of large datasets, Amazon Kinesis for real-time data streaming, AWS Glue for ETL (Extract, Transform, Load) workflows, and Amazon Redshift for data warehousing.
Google Cloud Platform (GCP)
Provides services such as Cloud Dataflow for batch and real-time data processing, Cloud Dataproc for Hadoop and Spark processing, Cloud Pub/Sub for real-time messaging, and BigQuery for data warehousing and analytics.
Microsoft Azure
Gives services such as Azure HDInsight for processing big data with Hadoop and Spark, Azure Stream Analytics for real-time data processing, Azure Data Factory for ETL workflows, and Azure Synapse Analytics for data warehousing and analytics.
Snowflake
Is a cloud-based data warehousing service that allows for easy and efficient storage, management, and analysis of large datasets. Thw users can scale their computing power independently from their storage capacity, allowing them to handle large amounts of data without needing to worry about capacity constraints.
dbt
Provides a way to define, test, and execute transformations on data in a way that is modular and maintainable. It does this by allowing users to define "models" in SQL, which represent tables or views in the data warehouse. These models can then be tested and executed using dbt's command-line interface.
Airflow
One of the key features of Airflow is its ability to create and manage complex workflows, which can involve tasks that need to be executed in a specific order and with specific dependencies. Airflow allows users to define these workflows using Python code and then schedule and monitor their execution using a web-based interface.
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