What are the different frameworks and applications used by a data engineer?
Last Updated :
25 Jun, 2024
In the modern world, which relies on large amounts of data, data engineers act as architects, who build and maintain the framework that facilitates the gathering, storage, and subsequent analysis of such data. This infrastructure is crucial to organizations so as to be in a position to discover new insights from data and then make the right decisions. Data engineers rely on numerous frameworks and applications in order to transform data into the right format for maximum accuracy and access. In this article, the author covers the main defining concepts, software, and tools that data engineers employ, along with actual examples.
Overview of Data Engineering Frameworks and Applications
A huge variety of data engineering frameworks and applications can be met, as the processes of data management are manifold. Some of these tools may be categorized based on functions such as data ingestion tools, data storage tools, data processing tools, toolkits for orchestrating end-to-end data pipelines, and frameworks for analyzing data.
- Data Ingestion Frameworks: These are used to gather information from multiple vectors, and the information could be in the form of databases, APIs, or streaming information.
- Data Storage Solutions: These include data repositories such as databases and data warehouses where the ingested data is stored and categorized.
- Data Processing Frameworks: These are applied for the data cleaning process, data transformation, and data aggregation.
- Orchestration Tools: These manage the flow of data through a pipeline to support rapid data transfer from stages of data ingestion through data processing to data storage.
- Analytics and Visualization Tools: They are used in processing and making the data more understandable through the provision of summaries, simplified graphs, etc.
Commonly Used Tools and Technologies
1. Data Ingestion Tools:
- Apache Kafka is a streaming multimedia delivery network that supports the processing of real-time data streams.
- Flume: A service used for amassing, indexing, and transporting copious amounts of log data.
- Apache NiFi: Enables strong and large results of directed graphs describing data routing, transformation, and system-mediated logic.
2. Data Storage Solutions:
- Hadoop HDFS is a high-throughput system to organize files that may be accessed by applications across the distributed system.
- Amazon S3: An object storage service that is deemed effective in terms of the aspects of scalability, data accessibility, security, and throughput.
- Google BigQuery is a fully managed automated data warehousing solution where a high-speed SQL query can be performed.
3. Data Processing Frameworks:
- Apache Spark: Apache Stratos: An organismic platform-based architecture that understands big data and includes components for stream processing, SQL queries, machine learning, and graph problems.
- Apache Flink is an integrated stream-processing environment that is capable of processing both a stream of events and files.
- Airflow is an open-source program with language support to write scripts, automate scheduling, and monitor workflows.
4. Orchestration Tools:
- Apache Airflow is popular as it creates a way for programmatically authoring, scheduling, and, therefore, monitoring workflows.
- Luigi: A simplified tool in Python for creating complex pipelines for running batch jobs.
- Prefect: A software tool that assists in controlling, running, and detecting issues related to data pipelines.
5. Analytics and Visualization Tools:
- Tableau: A safe, capable, and strong all-round analytics solution.
- Power BI: A set of business applications for creating reports and visualizations with tips on how to use and interpret data.
- Looker: According to the definition, a system for data discovery is used for exploration or analysis of data.
Use Cases and Examples
Use Case 1: In the case of real-time data processing, the data processing is carried out as soon as the data items are generated.
- Tool: Apache Kafka and Apache Spark are two such technologies that are being increasingly used as the backbone of complex systems.
- Example: A financial institution employs Kafka for the commencement of streaming transaction data feeds, whereas it employs Spark for real-time analysis and fraud detection.
Use Case 2: Batch Method of Data Processing for Analytical Purposes
- Tool: And thus you have Hadoop’s HDFS and Apache Airflow.
- Example: A third example, another e-commerce company, retains the clickstream data of the customers in HDFS and performs ETL operations using Airflow to prepare data for business intelligence.
This year, we rented increase-type warehouses, which are capable of as much rental growth, to further increase our recognition of the patient for the rent.
Use Case 3: Solving Approaches for Data Orchestration in a Cloud Environment
- Tool: Consistency with Google BigQuery and Perfect
- Example: A marketing firm utilizes BigQuery for the storage of big data involving customers’ conduct and Prefect for the management of the applicants’ jobs that involve preparing data for analysis.
Conclusion
Data engineering is another essential facet in today’s data environment, as it ensures that data becomes an important asset to companies. The tools and frameworks highlighted as crucial to data engineers represent a small subset of the vast catalog of technologies at their disposal. Chart mastery enables the data engineers to develop a well-organized, flexible, and optimized pipeline from which companies can get all the necessary data they need for effective performance.
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