You want to improve your data engineering skills. How can you build a better data architecture?

You're a Data Engineer you must knowing this points, if you already knowing this points you're a Data Engineer or else you're worker not a engineer, You can knowing your projects upstream and downstream then how your project help your stakeholders and company then knowing on how to complete a project within a deadline and what are the problem if you can face doing this project and how to solve the problem then how dividing a project within your team members.

Firstly, catalog all your data sources to have a clear view of where data originates. This includes internal systems like CRMs, ERPs, transactional databases, and external sources such as APIs from third-party services, IoT devices, or publicly available datasets. Secondly, assess the size of the data and how fast it accumulates. High-volume or high-velocity data may require more robust storage solutions (like data lakes or distributed databases) and efficient processing frameworks (like Apache Spark or Flink).

Try to understand the Source and Target landscape. create a s2t ( source to target ) documents to capture all the metadata details along with required transformation for ETL creation. Once it's done, classify the ETLs into Simple , Medium and Complex for re-estimating the timeline.

Understanding your data sources and requirements is a fundamental step in developing an effective data architecture. To understand it consider the following: Identify Stakeholders. Understand the overall business strategy, goals, and business needs. Identify principles that guide decision-making in data management. Evaluate the quality of existing data and identify areas for improvement. Identify master data entities. Create a catalog of existing data sources, including databases, files, and external data feeds. Create an inventory of existing data sources, specifying their characteristics and relationships. Identify requirements for data integration and interoperability. Choose proper technologies for data storage. Consider data lifecycle

Using Nosql Data base or SQL database to Store a data in database depending upon projects, because you want store a data in disbuttited computing storage format you can use Nosql Data base or else you want store a data in table wise using SQL database, some times you want consistency and partio tolerence you can use Nosql database at the same time you want consistency and availability you can use SQL database, so single project you can use both SQL database and Nosql Data base depending upon projects, so understanding your problem and knowing how to solve the problem.

Choose data storage and processing technologies that can handle real-time data streams efficiently. Prioritize solutions that offer low-latency processing, scalability, and fault tolerance to support continuous data ingestion and analysis. Consider options like Apache Kafka for stream processing, coupled with databases like Apache Cassandra or Amazon DynamoDB for storing and querying real-time data. Ensure these technologies align with your architecture's requirements for performance, reliability, and security in real-time data processing scenarios.

Microsoft Azure stands out as a formidable platform for data engineering for several reasons on my opinion. Its comprehensive suite of tools and services caters to a wide range of data engineering needs, offering solutions for data storage, processing, and analysis. Azure's Blob Storage, for example, provides highly durable and available non-relational data storage, while Azure Data Lake Storage offers a hierarchical storage solution built atop Blob Storage, ideal for big data processing systems like Azure Databricks. Azure Cosmos DB, another highlight, delivers a highly scalable non-relational database that supports multiple data models and consistency levels, ensuring flexibility and performance.

Understand Data Requirements: Data type, volume, velocity, variety, and access pattern. Define the use cases and workloads: Analytics, reporting, real-time, and batch processing. Data lifecycle: storage duration, archival, and retrieval requirements and process. Scalability and performance (latency/throughput) expectations. Data Security and data governance requirements. Evaluate storage technologies based. Cloud vs on-premises decision. Compatibility with API, connectors, and data integration tools. Total cost of ownership, licensing, and subscription costs. Future growth and flexibility of data and business. Community and vendor support.

Define a clear framework for ETL, one time load or history load for pipelines. Create reputable components. Define re start ability for workflows. Create run book for all schedule details. Make Audit Balance control for every ETL monitoring.

Identify the data sources and their formats: Understand the specific data sources that contribute to your pipeline. ... Choose the right data collection method: Consider whether real-time data processing is necessary for immediate insights or if periodic batch processing suffices.

Break down the data pipeline into small, independent services (microservices) with well-defined responsibilities. Ensure that components within the pipeline are loosely coupled, allowing for independent development, deployment, and scaling. To promote interoperability and easy integration API-based communications between modules are essential. Containerize individual services and orchestration tools (e.g., Kubernetes) to manage and coordinate the execution of multiple containers. For scalable design consider horizontal Scaling, partitioning and sharding, load balancing, and caching technologies. Employ efficient data transformation techniques. Implement an event-driven architecture. Support both Batch and stream processing.

Here, I am mentioning some tools for testing and monitoring data architecture. Apache Griffin, DataDog for data quality monitoring and testing. Data profiling tools are Apache Nifi, Trifacta etc. For Data Monitoring and Observability, there are several tools like Prometheus, Grafana, New Relic etc. Some workflow orchestration and monitoring tools are Apache Airflow, Luigi. Streaming Data Monitoring: Confluent Control Center, Amazon Kinesis Data Analytics. Collaboration and Documentation: Confluence, Slack.

- Understand the business needs and define clear goals - Define the data model based on requirements - Select appropriate technologies for ingestion, transformation, processing, storage considering factors like cost, performance, scalability. - Establish process for data governance, including data quality, security and compliance - Set up monitoring tools to track the performance of data architecture

1) Digging deep into how different technologies are better suited for individual requirement is also helpful, for example - MySQL vs MongoDB - Kafka vs RabbitMQ - OLAP vs OLTP This can be done by reading some white papers on the technology. 2) Data versioning - Understanding which data point came from where, and data linage becomes important in the data coming from different sources/systems.