What skills will future data engineers need?

The field of Data Engineering is expanding at an unprecedented pace. Relying solely on foundational tools like Python, SQL, and Spark will no longer suffice.Below is a comprehensive list of skills essential for anyone aspiring to be an elite data engineer in today's world ?? 1.) Foundational Skills: SQL, Python, BigData Fundamentals 2.) Cloud: AWS, GCP, Azure 3.) Databases: PostgreSQL, MongoDB 4.) Processing: Apache Spark, Flink, Beam, Databricks 5.) Orchestration: Apache Airflow, Mage, Kestra 6.) CI/CD: Git, Jenkins, Docker 7.) Cluster Management: Kubernetes 8.) ETL Tools: Informatica, DBT, AirByte 9.) Monitoring: Grafana, Splunk 10.) Streaming: Apache Kafka 11.) Lakes: AWS Lake Formation, Delta Lake 12.) Warehousing: Snowflake, BigQuery

Instead of overwhelming you with a long list of data processing tools, I recommend starting with Apache Beam. Think of it as the 'Docker' of data processing, offering a write-once, run-anywhere solution for handling mission-critical production workloads. While Apache Spark is widely known, in the world of Apache Beam, it's primarily a 'runner.' In fact, Beam supports various other runners like Apache Flink, Apache Samza, Apache Nemo, Hazelcast Jet, Twister2, and more. One of Beam's standout features is the ability to create cross-language transforms. This means that if your team is coding in Java, they can easily share their logic with teams using different languages like Python, Go, Scala, or Typescript.

I believe that the key skills for future data engineers in the cloud computing domain include proficiency in major cloud platforms, cloud-based data warehousing and data lakes, data integration tools, containerization technologies like Docker and Kubernetes, serverless computing, cloud security and compliance, scalability and elasticity, and real-time data streaming. These skills will empower them to create efficient, adaptable, and secure data solutions in the cloud.

Data engineers in 2023 and beyond must possess a diverse skill set to navigate the evolving data landscape. Proficiency in cloud technologies, big data tools, and real-time processing is paramount. Strong programming skills, ETL expertise, and database management are fundamental for data pipeline development and maintenance. Data governance and security are crucial in light of privacy regulations. Machine learning integration, data visualization, and soft skills enhance data-driven decision-making. Adaptability to new technologies and domains remains vital. Furthermore, awareness of generative AI technologies, like GPT-3, can boost productivity. In this dynamic field, staying informed and continually learning are key to future success.

Data engineering skills for future data engineers in cloud computing: ~Programming languages: Python, Scala, SQL ~Cloud computing platforms: AWS, Azure, Google Cloud Platform ~Big data technologies: Hadoop, Spark, Kafka, Hive, HBase ~Data warehousing and data lakes: Amazon Redshift, Azure Synapse Analytics, Google BigQuery, Amazon S3, Azure Blob Storage, Google Cloud Storage ~Data streaming technologies: Amazon Kinesis, Azure Event Hubs, Google Cloud Pub/Sub ~Data modeling and data governance: Entity-relationship modeling (ERM), data quality management (DQM), data lineage, data security ~Data engineering tools and technologies: Apache Airflow, Apache Oozie, AWS Glue, Azure Data Factory, Google Cloud Dataflow, Apache NiFi

Here are the things that a data engineer should know 1. Knowledge on RDBMS & SQL 2. one programming language preferably python 3. fundamentals of distributed storage 4. one distributed computing framework like Apache Spark / Azure Databricks 5. knowledge on one ingestion tool, can be on cloud like Azure Datafactory 6. Spark Structured Streaming / Azure Event hub to handle streaming data 7. Datawarehouse concepts 8. One NoSQL DB can be on cloud. 9. one orchestration tool like Apache Airflow

Data engineers should focus on: SQL Expertise: Master SQL for efficient data querying, manipulation, and database management. Understand complex joins, subqueries, and optimization techniques. Framework Familiarity: Gain proficiency in frameworks like Apache Spark, Apache Airflow, or Pandas. Understand their capabilities and best practices to efficiently process and transform large volumes of data. Error Handling & Debugging: Hone skills in identifying and rectifying errors in code. Familiarize yourself with debugging tools and techniques to enhance code quality and efficiency. Efficient Coding Practices: Learn and adopt efficient coding practices and design patterns to optimize code performance, readability, and maintainability.

Programming and scripting are essential in data engineering. Proficiency in at least one general-purpose programming language like (SQL), Python, Java, or newer performance-critical applications like Rust is needed. Familiarity with Bash, PowerShell, or the general Linux command line is highly beneficial, as everything runs on Linux-based environments. Frameworks like Apache Spark, Airflow, and Pandas are essential tools in data engineering. Newer ones like Dagster, Polars, DuckDB, and many others are constantly emerging. With added functional data engineering techniques, these skills help create valuable and reliable data pipelines, empowering businesses to make informed, data-driven decisions.

Spark and sql are obviously mandatory choice. I will mention two very important skill. 1. In depth knowledge of data modelling. 2. Data pipeline design skills for handelling large amount of data, how you choose resources for your cluster, what orchestration tool you select based on specific use case, these things comes with experience and very important skills to have

In my experience yes Apache Spark and Pyspark , Pandas knowledge is essential for modern data engineering dealing in cloud environments although with this SQL doesn’t seem to loose its important and skill in SQL would always be very useful and handy for a data engineers , my recommendation would be to start with SQL and build on that with python , sky is the limit.

Data modeling and design serve as the cornerstone of effective data management. It acts as a structural blueprint, bringing order to the data chaos. Beyond its organizational prowess, data modeling enhances data clarity, safeguards integrity, fuels agility, and turbocharges data processing. It's the secret sauce that elevates data architecture and engineering, making communication and management a breeze.

Understanding the end-use of data is crucial for engineers to develop effective pipelines from incomplete requirements. Familiarize yourself with dimensional modelling and its role in creating powerful multi-dimensional pivot tables with ROLAP, enabling business users to explore data. However, dimensional modelling may not be suitable for all cases and can lead to unintended consequences. Consider complementing it with Data Vault or normalized models, or even hybrid models, to maintain simplicity.

Without understanding the usage of the data, it is complicated for an engineer to extrapolate from incomplete requirements and build a good pipeline. Such, it's vital that you understand the downstream work. Learn about dimensional modelling and how that is used together with ROLAP to create powerful multi dimensional pivot-tables to empower business users to explore the data. Don't forget, conforming to dimensional modelling is unsuitable for many cases and can cause undesired side effects. Complement with Data Vault or normalized models, or create hybrid models, if it helps keep complexity low.

As we have seen in earlier 2 points, DE's have to focus on working with both type of systems -> OLTP -> OLAP What works on RDBMS system is not necessary will work well with non-RDBMS system like Apache Hadoop. For example -> Normalization & De-normalization -> Constraints -> Data sharding and splitting vs Partitioning and bucketing With time, we have seen industry moving sequentially in below order -> Data warehouse -> Data Lake -> Data Mesh So currently, DE's will have to start learning how Data Mesh behaves, understand terms like Data Governance, Data Contracts, Data Observability, domain owners, business owners etc. With time, this will keep evolving and DE's have to remain up to date to adopt and implement it.

Somehow i see myself repeating over and over. It all boils down to fundamentals and ability to adapt. Some companies need a star schema, some need a normalized approach and some will need a data Vault. Knowing differences will give you an edge, since the market is full of star schema modelers.

Ensuring data quality and maintaining robust data governance practices will become even more critical in the future. Data engineers should be skilled in implementing data quality checks, data profiling, and data cleansing processes. They must understand data lineage and be able to trace data from its source to its destination, ensuring data integrity and compliance with regulations such as GDPR. Knowledge of data cataloging tools and metadata management is also valuable for maintaining a clear inventory of data assets.

Data lineage is a crucial component of data management and governance strategies, helping organizations maintain data quality, comply with regulations, and make informed decisions based on trusted data. It also aids in troubleshooting and improving the efficiency of data pipelines and also ensuring we are not working with varying source of truths which is a common challenge

Data quality and Governance is so important because certain type of data can be so sensitive and it can fall into category of blinding/unblinding for which only few fields can be exposed

How to get started with governance and data quality? 1. add "load id/time" and "source" to your ingested data so you know where any row of data comes from 2. Track schema changes so you know where columns come from. 3. Create contracts, and assign responsible curators and producers for the data under contract

Future data engineers, especially those involved in AI/ML work, must address the overlooked challenge of data drift, where data distribution changes over time, potentially affecting AI/ML model performance. Emphasizing real-time monitoring of data for shifts in patterns, integrating automated anomaly detection in data streams, and devising adaptive data pipelines that can self-adjust to evolving data patterns will be pivotal. Addressing data drift not only ensures the robustness of data quality and governance but also guarantees the consistent performance of AI/ML models in dynamic business environments.

Gaining knowledge in the domain and context of work is important as it affects the engineering goals. It will guide in extracting information from raw data following logical aggregations and which data fields should be considered, and how to identify cases like out-layers. Referring to research papers related to the domain in focus could lead to a scientific selection for the correct features.

Data engineers of the future will not operate in isolation but will collaborate closely with data scientists, analysts, and business stakeholders. Strong data visualization skills will be essential to convey complex technical concepts to non-technical audiences. Proficiency in tools like Tableau, Power BI, or Python libraries for data visualization will be valuable for creating informative and visually appealing reports and dashboards. Effective communication skills will enable data engineers to bridge the gap between technical and business teams, ensuring that data solutions align with organizational goals.

The choice of tech stack often depends on specific business needs, existing infrastructure, team expertise, and budget constraints. However, familiarizing oneself with these tools and platforms ensures a comprehensive and modern approach to data engineering challenges. Visualization Tools: Tableau, Microsoft Power BI, Looker, Grafana. Storytelling: Data-driven storytelling tools like Google Data Studio, and narrative platforms like Medium or internal documentation platforms.

I am kicking off a GenAI project this week with a customer who has never been able to properly see business insights in anything except for tableu. In rolling out the capabilities of Google Clouds “looker” that we are implementing it was tough for this Dir.IT to not do a backflip for what was shown we will be able to pull from his historic data. Visualization is key for learners like me and it was no different for this technology leader.

Data is useless without its meaning. As the volume of data increases, it becomes utmost important to have data visualization tools to find meaningful patterns, graphs and more. Data visualization tools such as Tableau is very helpful to get meaningful insights out of heaps of data in fraction of minutes.

Future engineers will need to focus more on basics instead of tech. Start by Database design, data modeling OLAP/OLTP Python (easy but you need to learn how to code the pythonic way following SE best practices) SQL -> From SELECT -> Window Functions (Don't forget about MERGE) -> PL/SQL as well Shell and commands Then comes distributed computing Testing/ Data quality Git (Versioning) And lastly HOW TO WRITE/READ Docs -> this is key to learn any tech Then there is CLOUD and all the fun stuff

I’m addition the the universally/ generally accepted skills like Python, Spark, SQL, Airflow etc, having a firm grip on both the data modelling aspects and the non-functional concepts such as resilience, idempotence, performance optimization can help data engineers stand out. Also- having knowledge of using/ integrating with various APIs to ingest data is something that’s becoming more of a norm, and can help data engineers form richer datasets!

Data engineers should tackle the data challenges with a platform/cloud agnostic approach. No matter what platform we use, the fundamental challenges faced by data engineers remain the same. Below are a few examples: -Scaling, -Optimization, -Orchestration, -Security, -Performing historic data loads, -Incremental data loads, -Realtime/Near realtime streaming, -Understanding the business problem we are trying to solve, -Understanding the data from various source systems, -Various data formats, their pros and cons, -Integrating with reporting, data science frameworks etc. Once we get hold of the above concepts, any current or future technology platform or language will become just a medium to express our thought process and solution design.

The skills needed are expanding. Yeah, cloud stuff and coding are still key, but there's more brewing. Getting cozy with machine learning; Understanding how to integrate machine learning models into data pipelines and working with AI-driven systems will likely become standard for data engineers. As the volume of data ever increases, expertise in handling big datasets, and keeping data safe (think cybersecurity) is becoming a must. Also, knowing how to streamline things with DevOps and being savvy about ethical data use is gaining traction. And hey, good communication matters too! Future data engineers have got to blend tech smarts with teamwork and good ethics for the win.

Career paths are not always linear, and individuals may transition between roles and industries based on their interests and opportunities. Networking, mentorship, and continuously expanding your skill set are essential for success in the field of data engineering. As data engineers become more senior, they may be given opportunity to take on the Data Engineering Manager role to manage data engineering teams, and shift from hands-on technical work to people and project management. Individuals with deep understanding of data management concepts, data modeling design techniques, data security, data governance best practices, and excellent communication skills to work with business leaders, could transition into the Data Architect role.