Elasticsearch is one of the most used and popular search engines and distributed analytical tools used to manage, store, and search all kinds of data including structural and unstructured data. It enables full-text searches and a number of other complicated actions. It stores and manages the data in a document in JSON format and uses JSON-based queries and index APIs to manage documented data. Large amounts of raw and semi-structured data need to be managed, so a specialized tool like ETL tool is needed.
This blog will elaborate on:
What is ETL?
The ETL is a combination of three processes; “Extraction”, “Transformation”, and “Data Loading”. It is a process of integrating data from different sources into a unified/single data warehouse. The ETL process involves the following sequence:
Extraction
It includes the extraction of data from various sources.
Transformation
Data transformation includes cleaning up the data and eliminating redundancy to change the data into the proper shape.
Loading
It is the final process to load the transformed data to the data warehouse:
The Elasticsearch users are usually required to perform ETL on raw and unstructured data for data analytics and move data to the data warehouse. But doing ETL manually is quite difficult, complex, and may have limitations.
Limitations of the Manual ETL Process
The ETL process can be done manually through spreadsheets and scripts as well as automatically through ETL tools. But the manual process of ETL may be inefficient, accurate, and quite difficult. The following are some limitations of doing the ETL process manually:
Lack of Scalability
As ETL process is difficult for the vast amount of raw data and as it is a labor-intensive and consuming task.
Security Limit
A large amount of data is difficult to handle and may contain sensitive data such as user personal information or may contain financial information.
Costly
It requires high expertise that may be costly.
Error
Manual ETL processes can be inefficient, error-prone; however, users can avoid such manual ETL process blunders by using smart ETL tools.
Best Tools of ETL for Elasticsearch
There are numerous ETL tools available for Elasticsearch that automates the ETL task and some of them are listed below:
Logstash
Logstash is an open-source official product of the Elastic community that is utilized to manage, collect, and store data. Additionally, it is used to transform and filter data using native codecs, plugins, and filters. It is the core component of the ELK elastic stack. You can get Logstash from the official Elastic website for free.
Apache NiFi
The Apache NiFi is a completely open-source data transformation and integration tool introduced in 2014 by NSA. NiFi offers a simple user interface for planning, controlling, and keeping track of data flows across systems. It supports various data sources including web services, databases, and handles real-time data streams. It can be installed from its official website.
Hevo Data
Hevo Data is a subscription-based powerful ETL tool. It is a platform for cloud-based data integration that provides a user-friendly interface and makes it suited for non-technical users to set up data pipelines and automate data integration operations. To get the Hevo Data tool or to learn more about the tool and pricing detail, navigate its official website using the attached link.
That’s all about the Elasticsearch ETL tool.
Conclusion
The ETL is the process of data extraction, transformation, and data loading. It is used to integrate data from several sources into a single or unified data warehouse. Various ETL tools are being used to automate the ETL process some of them are Logstash, Apache NiFi, and Hevo Data. This post has described what ETL is and some best ETL tools.
