You’re starting a new data engineering project. What are the key techniques you need to know?

In experience I have seen doing extensive analysis on your source data avoid reworking on your code.. Try to understand in detail how the data and the business process behind the data works.. this can avoid data anomalies and gives more confident data to the end user.

In my data engineering project: goal - "To reduce customer churn by 10% within the next quarter through predictive analytics to identify at-risk customers and implement targeted retention strategies." The scope - data collection on customer behavior and purchase history, analyzing this data to pinpoint churn predictors, developing personalized retention strategies, and measuring their effectiveness against the churn rate reduction target. This targeted approach helped maintain focus, ensured efficient use of resources, and provided clear success metrics. It also helped me prioritize, eliminating distractions by irrelevant data, focusing solely on key factors influencing customer retention.

Ensure you have a clear goal that has a clear measurable outcome. These measurable outcomes are key to communicating business value. Vague outcomes such as automate and increase efficiency without putting numbers to it can be hard to justify. With a clear goal and outcome, sit with business domain experts and data stewards to get a good idea of the processes you will be working with, the range of data involved as well as the standards and quality currently being enforced. This will give you a good idea of how much preparatory efforts are needed. I usually assign a discovery period for each project before the scope is finalised and work begins.

In the Data Engineering field, we should focus more on the Ingestion, Cleaning, and Processing parts. Data Engineering is separate from Data Analyst and Data Science. So, the Data Engineer should not go deep into the reporting as well as the Modeling part. These are the boundaries I can think of.

🚀 Our data engineering journey began with a clear mission: dissect firewall traffic to boost security and optimize resources. We outlined objectives, pinpointed data sources (firewall logs), and set our sights on real-time visualizations 📊. Early stakeholder engagement ensured alignment, guiding us through challenges with our clear scope 🧭. Swift anomaly detection became our forte with real-time dashboards, all thanks to defined goals and effective communication 📈. As we navigated the project landscape, collaboration led us to uncover insights and foster a culture of innovation 🌟. Together, we drove continuous improvement in our data engineering practices, turning challenges into opportunities for growth 🌱

Tools and platforms selection is essential. To my knowledge: AWS, Azure, and GCP : lead in cloud services, S3, Blob, Google Cloud Storage : robust solutions for data storage , EMR, HDInsight, Dataproc : processing, Step Functions, Logic Apps, Composer : orchestration, and QuickSight, Power BI, Data Studio : visualization. Python remains the go-to for its libraries and frameworks, Spark for large-scale data processing, Airflow for workflow orchestration, and Kafka for real-time data streaming. Each tool/platform has its niche: AWS's scalability, Azure's integration with Microsoft products, GCP's analytics capabilities, Python's versatility, Spark's speed, Airflow's scheduling, and Kafka's streaming precision.

In my experience, if the business is in near real time, I will choose the following tools and platforms:  Azure Datafactory for Extract Transform Load  Databricks for performing transformations Airflow for orchestration, or if it is quite simple, I could rely on ADF triggers.  Power BI for Visulization  Azure Synapse if the business has a data-housing use case. Overall, on a basic I would need SQL, Python, and PySpark skills.

When selecting tools and platforms for a data engineering project, consider the factors mentioned. Common choices include cloud-based services (AWS, Azure, GCP) for storage, processing, orchestration, and visualization, along with programming languages like Python, frameworks like Spark, and tools like Airflow or Kafka.

🛠️ Embarking on a data engineering quest requires careful selection of tools and platforms tailored to your goals and scope. Consider the intricacies of data volume, velocity, variety, and veracity 📊. Cloud-based services like AWS, Azure, or GCP offer a plethora of components for storage, processing, orchestration, and visualization 🌐. Choose wisely based on factors like complexity, cost, and compatibility. Delve into programming languages, frameworks, and libraries that align with your project needs and skill set. Whether it's Python 🐍, Spark ⚡, Airflow 🌬️, or Kafka 📻, the right tools empower you to conquer data engineering challenges with finesse.

As before startimg any Data Engineering Project, few things must be clear out along with the techniques and Technologies you are going to put forward. 1. Understand the client requiremnt , Scope of Project deliverables for each milestone and the end goal of the project. This ensures to create a baseline for the type of approach or work you have to follow for that project. 2. Analyze thoroughly the client requirements and their existing landscape and then accordingly extract out relevant services amd solution architecture for that particular Project. 3. Align the right fit of services and get a sign off frlm customer on the proposed architecture. 4. Design Low Level Frameworks , Automated Orchestration process ,pipelines, test cases

📐 Now that your toolkit is primed, it's time to architect your data model and pipeline. Think of your data model as the blueprint 🏗️, detailing how your data is structured, organized, and interconnected. Your data pipeline, on the other hand, is the sequence of steps orchestrating data ingestion, transformation, and delivery 🔄. As you design, keep these guiding principles close: simplicity, scalability, modularity, reusability, and maintainability. Strive for elegance in your architecture, ensuring it can grow seamlessly with your needs 🌱. Remember to document your design meticulously and embrace version control to track changes and collaborate effectively.

Designing a data model and pipeline is pivotal post-tool selection. A data model structures and relates data, while a pipeline executes ingestion, transformation, and delivery. Design with principles like simplicity, scalability, modularity, reusability, and maintainability. Document your design and leverage version control for collaboration.

Starting a new data engineering project requires a solid grasp of various techniques and concepts to ensure the design, development, and maintenance of robust data systems. Here are key techniques you should be familiar with: 1. Data Modeling 2. ETL Processes 3. Storage Solution 4. Data Partitioning and Indexing 5. Data Governance and Metadata Management

Choose the data model according to the input data streams. As it would become difficult if there is any data loss during conversion for processing purpose. I believe that each data source should be inspected before finalizing the data model so that most of the data issues could be avoided. As the input data stream is not in control but the output data could be managed.

Start with Ingest for pipeline - ensure you have an existing pattern or if you need to define a new pattern for the kind of data you are ingesting main types to think about: 1) Batch 2) Streaming 3) API -(could be in same approach as batch) Decide on target for ingest. List out the source systems to bring in, and focus on the highest priority one first. All this can be done, without needing the business context. however you should start to investigate what the target state of your pipeline looks like as well. This approach lets you get started quickly, without major planning or alignment delays on target state. You can then decide on transform and whether you are integrating directly, or using another tool for transforms.

In my experience, thoroughly testing and validating your data is crucial for data engineering. You gotta be on top of checking that data is complete, accurate, and consistent at every step - automate it with tools like pytest or Great Expectations. Don't skip this - bad data leads to bad decisions. Make data testing a priority from the start. It powers effective data-driven decisions for your organization

🔍 As our data engineering saga unfolds, the next chapter beckons: testing and validating our data 🛠️. It's a crucial technique, akin to polishing a gem 💎, ensuring our data is pristine and fit for purpose. At every twist and turn of our data pipeline, from ingestion to transformation and delivery, we meticulously test and validate. We scrutinize for completeness, accuracy, consistency, and relevance, aligning each data piece with our lofty goals and scope 🎯. To aid us in this noble quest, we enlist the aid of automated tools and frameworks like pytest, unittest, and Great Expectations. These stalwart companions tirelessly perform data quality checks, generating reports that illuminate any imperfections ✨.

Testing and validating data is essential in data engineering projects to ensure completeness, accuracy, consistency, and relevance. This involves checking data at various pipeline stages like ingestion, transformation, and delivery. Automated tools like pytest, unittest, or Great Expectations aid in data quality checks and report generation.

Data validation is a critical component of any data pipeline. While modern tools streamline data handling, implementing robust data quality checks early in the pipeline is essential. This proactive approach ensures that only clean and accurate data reaches the final destination, be it a data lake or database, ultimately empowering data-driven decision-making.

Leverage dbt's built-in data testing framework to define and automate assertions on data freshness, schema integrity, and business rules across sources and models. Utilize dbt's automated documentation capabilities to generate up-to-date data lineage and catalog information, improving transparency and collaboration. Integrate dbt with version control systems like Git to track changes, enable code reviews, and enforce testing across data pipelines through CI/CD processes. Copy

In data engineering projects, monitoring and optimization are critical for ensuring data quality, performance, and cost-effectiveness. To monitor data, set up automated checks for data quality, consistency, and completeness. Use logging and monitoring tools to track data pipelines and identify issues promptly. For optimization, focus on improving data processing efficiency, such as optimizing queries, using indexing, and partitioning data. Consider data compression and storage optimization techniques to reduce costs. Regularly review and refine data pipelines based on performance metrics to ensure they meet evolving business needs.

📊 As our data engineering odyssey continues, another vital chapter unfolds: monitoring and optimizing our data 🔄. It's akin to fine-tuning a symphony 🎶, ensuring every note resonates with precision and harmony. With vigilant eyes, we measure and enhance the performance, reliability, and efficiency of our data pipeline. Metrics such as data throughput, latency, error rate, resource utilization, and cost become our guiding stars 🌟. To aid in our quest for optimization, we harness tools and platforms like Grafana, Prometheus, and CloudWatch. These instruments allow us to collect, visualize, and analyze our data metrics, uncovering hidden bottlenecks and seizing opportunities for improvement 🛠️.

Monitoring and optimizing data is essential in data engineering projects to enhance performance, reliability, and efficiency. Metrics like throughput, latency, error rate, resource utilization, and cost are crucial for assessment. Utilizing tools like Grafana, Prometheus, or CloudWatch aids in data collection, visualization, and analysis, pinpointing bottlenecks and avenues for enhancement.

Implement custom monitoring solutions by building webhooks or integrations with collaboration tools (Slack, Microsoft Teams) to receive real-time notifications on pipeline status, errors, or SLA breaches. Develop tailored data visualization dashboards using tools like Grafana or Superset to track key metrics such as data throughput, latency, resource utilization, and costs across your pipelines. Leverage auto-scaling capabilities of cloud platforms and orchestrators like Kubernetes to dynamically optimize resource allocation based on monitoring data, improving cost efficiency while maintaining performance.

Here are some techniques to consider - 1. Data Profiling: Analyze data structure, quality, and distribution. 2. Performance Monitoring: Track latency, throughput, resource use. 3. Logging & Auditing: Record data flow, transformations, system activities. 4. Alerting & Notification: Set up alerts for critical events.

1. Don't underestimate the time. Application development and Data Engineering is not same. 2. Right team where everyone talk, see, act as data engineering is important. Team includes QA, Product Manager, Project Manager, UX designer, Content writer - apart from the core data engineers.

The key techniques in data engineering are ETL, data modelling, streaming, Lambda, warehouse/lake, data security and cloud storage. In data modelling we should know both data vault and dimensional model. For data security we should know about authentication, access control, encryption, data recovery, data privacy and threat detection. For ETL (Extract-Transform-Load) we need to know about joining and splitting data, data aggregation and derivation, incremental loading, Change Data Capture (CDC) and ELT (Extract-Load-Transform). At the end of the day, data engineering is about designing data stores and building data pipelines. And after they are built, we monitor the data flow and data quality.

Learn the basics of software engineering as data engineering is just a part of software engineering. Also knowing a bit of other areas such as devops, DBA, backend and the core of what your company does ( finance, banking, medical, ...) will be very helpful.