1. Data ingestion: This is the process of gathering and importing data from various sources into the pipeline. Data can be ingested using tools such as Apache NiFi or Logstash.
2. Data storage: Once ingested, data needs to be stored in a scalable and reliable storage system. Common storage systems used in data pipelines include distributed databases like Hadoop Distributed File System (HDFS) or cloud-based storage solutions like Amazon S3.
3. Data processing: After data is stored, it needs to be processed and transformed into a format suitable for analysis. This can involve cleaning, filtering, and aggregating the data. Data processing tools include Apache Spark, Apache Flink, and Apache Beam.
4. Data analytics: Once the data is processed, it's ready for analysis. Analytics tools like SQL engines (e.g., Presto or Apache Hive) or machine learning libraries (e.g., TensorFlow or scikit-learn) are used to analyze the data and derive insights from it.
5. Data visualization: Finally, the results of the analytics are visualized using tools like Tableau or Power BI, allowing stakeholders to understand and make decisions based on the insights derived from the data.
I like to think of it as a continuous flow of information that moves through these different stages, enabling businesses to make data-driven decisions.