- Data Collection and Integration
Collecting data from various sources, including databases, APIs, external data providers, and streaming sources. They must design and implement efficient data pipelines to ensure a smooth flow of information into the data warehouse or storage system.
- Data Storage and Management
Once the data is collected, data engineers are responsible for its storage and management. This involves choosing appropriate database systems, optimizing data schemas, and ensuring data quality and integrity. They also must consider scalability and performance to handle large volumes of data.
- ETL (Extract, Transform, Load) Processes
Design ETL pipelines to transform raw data into a format suitable for analysis. This involves data cleansing, aggregation, and enrichment, ensuring the data is usable for data scientists and analysts.
Dealing with big data is the norm rather than the exception. working with big data technologies such as Hadoop and Spark to efficiently process and analyze massive datasets.
working with NoSQL databases like MongoDB and Cassandra, which are well-suited for handling unstructured or semi-structured data.
leveraging Cloud platforms to build scalable and cost-effective data solutions.
handling huge data volumes and ensure fault tolerance. Understanding how distributed systems work .
working with streaming technologies like Apache Kafka to handle and analyze data as it flows in.
DatabasesA deep understanding of relational databases (e.g., MySQL, PostgreSQL) and NoSQL databases (e.g., MongoDB, Cassandra) is crucial. Data engineers must choose the right database systems for specific use cases and design efficient data schemas. Big Data technologies like Hadoop, Spark, and Hive. These tools enable the efficient analysis of large datasets. ETL Tools like Apache Nifi, Talend, and Apache Airflow , Meltano, Greenplum and dbt Core. are essential for building data pipelines. Distributed SystemsData engineers need a solid grasp of distributed systems concepts to design scalable and fault-tolerant data architectures.HadoopKafkaData Warehousing Get a grasp of building and working with a data warehouse.Data Architecture must have the knowledge to build complex business database systems.Operating System well-versed in operating systems like UNIX, Linux, Solaris, and Windows.