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Exporting Elasticsearch data to CSV - Tool Skedler

The Best Tools for Exporting Elasticsearch Data to CSV

Are you looking for a seamless solution to extract and convert your valuable Elasticsearch data into CSV files? If you find yourself in the midst of Elasticsearch data, whether it’s infrastructure logs or security records, and you’re eager to export it to CSV for seamless analysis in tools like Excel or others, you’ve arrived at the perfect destination. In our quest to optimize your data analysis workflow, we’ll delve into the world of tools that stand at the forefront of exporting Elasticsearch data to CSV. Join us on this journey as we unlock the potential of your Elasticsearch data and convert it into a format that’s as flexible as it is powerful.

In this article, we will introduce you to the top tools in the market for exporting Elasticsearch data to CSV format.

Export data directly from Elasticsearch

Elastic - ElasticSearch Data to CSV/Excel

(Source: Roberto Sorin / unsplash)

In today’s data-driven world, the ability to harness the immense value contained within your Elasticsearch repository is paramount. Imagine you have a wealth of infrastructure logs or security data tucked away in Elasticsearch, and you’re yearning to unlock its full potential. This is where exporting data to a CSV format comes into play as a game-changer. Whether you’re looking to analyze this data in Excel, feed it into other data-driven tools, or simply need a more accessible and versatile format, exporting to CSV is the gateway to a world of possibilities.

Why is exporting Elasticsearch data to CSV so crucial, you might wonder? Well, for starters, it allows you to transcend the limitations of Elasticsearch and explore your data in environments where it can truly shine. By exporting Elasticsearch data to CSV, you’re granting yourself the freedom to perform advanced analyses, create insightful visualizations, and share your findings with stakeholders in a format they understand.

In this article, we present to you the top two tools for this crucial task. You no longer need to grapple with the intricacies of Elasticsearch data extraction because we’ve done the legwork for you. These tools will not only simplify the process but also enhance your ability to make the most of your Elasticsearch data. These are the best tools for exporting ElasticSearch data:

  1. Es2csv
  2. Python Pandas
  3. Elasticsearch Data Format Plugin

1. es2csv

es2csv is a dynamic command-line utility, coded in Python, dedicated to querying Elasticsearch using either Lucene query syntax or Query DSL syntax. Its core function is to retrieve results from Elasticsearch queries and export them as documents in a CSV file. This versatile tool enables the query of bulk documents across multiple indices, focusing on specific fields to enhance query execution efficiency.

Pros of es2csv

Here are the most essential advantages of es2csv.

  • Easy to install and configure
  • It can query bulk documents across multiple indices, extracting only the desired fields, significantly reducing query execution time.

Cons of es2csv

  • Limited Version Support: es2csv exclusively supports Elasticsearch versions 2x and 5x, lacking compatibility with more recent 6.x and 7.x versions.
  • You need Python 2.7.x and pip. So you must install a Python environment on your system.
  • It is a difficult tool to use for non-technical users.
  • To automate periodic data export, you need to write your own cron job.

2. Python-Pandas

Python Pandas is an open-source Python package known for its versatility. It takes data export from Elasticsearch to a new level, offering built-in functions that effortlessly convert your data into CSV, Excel, or HTML formats, facilitating seamless data exploration and analysis.

One of the advantages of having a flexible database and using Python’s Pandas Series is being able to export documents in a variety of formats. When you use Pandas IO Tools Elasticsearch to export Elasticsearch files in Python, you can analyze documents faster.

This requires the following prerequisites:

  1. Install Python
  2. Install pip
  3. Pip install Elasticsearch
  4. Pip install numpy
  5. Pip install Pandas

Pros of Python Pandas

  • Since it is written in Python, Python Pandas streamlines document generation, reducing code volume compared to Node.js.
  • Supports Elasticsearch version 7.x, ensuring your data remains accessible

Cons of Python Pandas

  • Prerequisites include Python setup.
  • Inability to export values with specific queries.
  • Custom automation setups need scripting. Learn how report automation can benefit your business and enhance the efficiency of your data analysis process. 
  • This tool is intended for users with technical backgrounds, mainly developers and data scientists, and may not be suitable for those without technical knowledge.

3. Elasticsearch Data Format

Elasticsearch Data Format is a valuable Elasticsearch plugin that expands your data export options. To utilize it, you’ll need to incorporate and configure it within your Elasticsearch plugins. This plugin simplifies the process of downloading search result responses in various formats, offering more than just the standard JSON. It supports CSV, Excel, and JSON (Bulk) formats. However, there are specific prerequisites for its usage, including Elasticsearch 5.x or below and a configured JAVA_HOME path.

Data. How to export data from ElasticSearch?

(Source: Claudio Scharwz / unsplash)

Pros of Elasticsearch Data Format

  • Easy to install.
  • It integrates seamlessly as an Elasticsearch plugin.
  • Uses simple curl commands and arguments.

Cons of Elasticsearch Data Format

  • Limited response format
  • It supports Elasticsearch versions only up to 5.x, limiting compatibility with more recent versions.
  • Users lacking technical proficiency may find it challenging.

Final Thoughts

Exporting ElasticSearch Data

(Source: Joshua Sortino / unsplash)

Our exploration of data export tools revealed a common challenge: the inability to export specific fields defined in queries, often leading to unwieldy data manipulation. Furthermore, it’s worth noting that among the tools we reviewed, only Python Pandas boasts compatibility with the latest Elasticsearch versions, specifically those exceeding version 5.x. This compatibility can be a game-changer when working with the most up-to-date Elasticsearch environments.

However, there are solutions such as Skedler Reports that stand out by allowing a precise selection of fields for export from Elasticsearch to CSV and Excel.  While open-source tools offer powerful features, they may primarily suit technical users. Skedler Reports bridges this gap, offering an accessible, automated solution for exporting Elastic Stack data in various formats, including CSV, XLS, and PDF. You can experience its capabilities through a free trial.

If you want to learn more about the benefits of Skedler and report automation in Elasticsearch, Kibana, or Grafana, we’ve covered it all in our article ‘Streamlining Data Reporting with Automation: Enhancing Efficiency with Skedler.’ Check it out!


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