Docker logging presents challenges due to the transiency, distribution, and isolation of containers, but the ELK Stack (Elasticsearch, Logstash, Kibana) offers a solution for centralized logging. Setting up an ELK pipeline involves pulling logs from Docker containers using Logstash, indexing them with Elasticsearch, and visualizing them in Kibana, although variations exist such as using different log shippers or adding a buffer layer like Kafka or Redis. The setup can be done on local or remote machines, or directly within a Docker environment, with considerations for resource consumption and networking for production environments. Filebeat, a lightweight log shipper, and Docker's logging drivers are common methods to ship logs into ELK, each with their own configurations and advantages. Parsing the data using Logstash involves configuring input, filter, and output sections to handle various log formats, with trial and error often needed. While ELK has been popular, Elasticsearch and Kibana's move to closed source has led to alternatives like AWS's OpenSearch, and platforms like Logz.io provide managed solutions with added features to handle scaling challenges. This article marks the first in a series, focusing on setting up the ELK stack for Docker logs, with subsequent parts addressing analysis and visualization.