How to load data from Elasticsearch to MS SQL Server

Learn how to use Airbyte to synchronize your Elasticsearch data into MS SQL Server within minutes.

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Set up a Elasticsearch connector in Airbyte

Connect to Elasticsearch or one of 400+ pre-built or 10,000+ custom connectors through simple account authentication.

Set up MS SQL Server for your extracted Elasticsearch data

Select MS SQL Server where you want to import data from your Elasticsearch source to. You can also choose other cloud data warehouses, databases, data lakes, vector databases, or any other supported Airbyte destinations.

Configure the Elasticsearch to MS SQL Server in Airbyte

This includes selecting the data you want to extract - streams and columns -, the sync frequency, where in the destination you want that data to be loaded.

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TL;DR

set up Elasticsearch as a source connector (using Auth, or usually an API key)
set up MS SQL Server as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Elasticsearch

Elasticsearch is a distributed search and analytics engine for all types of data. Elasticsearch is the central component of the ELK Stack (Elasticsearch, Logstash, and Kibana).

What is MS SQL Server

Microsoft SQL Server is a relational database management (RDBMS) built by Microsoft. As a database server, its primary function is to store and retrieve data upon the request of other software applications, either from the same computer or a different computer across a network—including the internet. To serve the needs of different audiences and workload sizes, Microsoft offers multiple editions (at least 12) of its Microsoft SQL Server.

Integrate Elasticsearch with MS SQL Server in minutes

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Prerequisites

A Elasticsearch account to transfer your customer data automatically from.
A MS SQL Server account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Elasticsearch and MS SQL Server, for seamless data migration.

When using Airbyte to move data from Elasticsearch to MS SQL Server, it extracts data from Elasticsearch using the source connector, converts it into a format MS SQL Server can ingest using the provided schema, and then loads it into MS SQL Server via the destination connector. This allows businesses to leverage their Elasticsearch data for advanced analytics and insights within MS SQL Server, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Elasticsearch as a source connector

1. Open the Airbyte UI and navigate to the "Sources" tab.
2. Click on the "Create Connection" button and select "Elasticsearch" as the source.
3. Enter the required information such as the name of the connection and the Elasticsearch URL.
4. Provide the Elasticsearch credentials such as the username and password.
5. Specify the index or indices that you want to replicate.
6. Choose the replication mode, either full or incremental.
7. Set the replication schedule according to your needs.
8. Test the connection to ensure that the Elasticsearch source connector is working correctly.
9. Save the connection and start the replication process.

It is important to note that the Elasticsearch source connector on Airbyte.com requires a valid Elasticsearch URL and credentials to establish a connection. The connector also allows you to specify the index or indices that you want to replicate and choose the replication mode and schedule. Once the connection is established, Airbyte will replicate the data from Elasticsearch to your destination of choice.

Step 2: Set up MS SQL Server as a destination connector

1. Open the Airbyte platform and navigate to the "Destinations" tab on the left-hand side of the screen.
2. Scroll down until you find the "MSSQL - SQL Server" connector and click on it.
3. Click on the "Create new destination" button.
4. Fill in the required information, including the destination name, host, port, database name, username, and password.
5. Click on the "Test connection" button to ensure that the connection is successful.
6. Once the connection is successful, click on the "Save" button to save the destination.
7. Navigate to the "Sources" tab on the left-hand side of the screen and select the source that you want to connect to the MSSQL - SQL Server destination.
8. Click on the "Create new connection" button.
9. Select the MSSQL - SQL Server destination that you just created from the drop-down menu.
10. Fill in the required information for the source, including the source name, host, port, database name, username, and password.
11. Click on the "Test connection" button to ensure that the connection is successful.
12. Once the connection is successful, click on the "Save" button to save the connection.13. You can now start syncing data from your source to your MSSQL - SQL Server destination.

Step 3: Set up a connection to sync your Elasticsearch data to MS SQL Server

Once you've successfully connected Elasticsearch as a data source and MS SQL Server as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Elasticsearch from the dropdown list of your configured sources.
Select your destination: Choose MS SQL Server from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Elasticsearch objects you want to import data from towards MS SQL Server. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Elasticsearch to MS SQL Server according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your MS SQL Server data warehouse is always up-to-date with your Elasticsearch data.

Use Cases to transfer your Elasticsearch data to MS SQL Server

Integrating data from Elasticsearch to MS SQL Server provides several benefits. Here are a few use cases:

Advanced Analytics: MS SQL Server’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Elasticsearch data, extracting insights that wouldn't be possible within Elasticsearch alone.
Data Consolidation: If you're using multiple other sources along with Elasticsearch, syncing to MS SQL Server allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Elasticsearch has limits on historical data. Syncing data to MS SQL Server allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: MS SQL Server provides robust data security features. Syncing Elasticsearch data to MS SQL Server ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: MS SQL Server can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Elasticsearch data.
Data Science and Machine Learning: By having Elasticsearch data in MS SQL Server, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Elasticsearch provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to MS SQL Server, providing more advanced business intelligence options. If you have a Elasticsearch table that needs to be converted to a MS SQL Server table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

Configure a Elasticsearch account as an Airbyte data source connector.
Configure MS SQL Server as a data destination connector.
Create an Airbyte data pipeline that will automatically be moving data directly from Elasticsearch to MS SQL Server after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

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Sync with Airbyte

Step 1: Set up Elasticsearch as a source connector

Step 2: Set up MS SQL Server as a destination connector

Step 3: Set up a connection to sync your Elasticsearch data to MS SQL Server

Once you've successfully connected Elasticsearch as a data source and MS SQL Server as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Elasticsearch from the dropdown list of your configured sources.
Select your destination: Choose MS SQL Server from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Elasticsearch objects you want to import data from towards MS SQL Server. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Elasticsearch to MS SQL Server according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your MS SQL Server data warehouse is always up-to-date with your Elasticsearch data.

How to Sync Elasticsearch to MS SQL Server Manually

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Step 2:

Step 3:

Step 4:

Step 5:

Step 6:

Step 7:

Step 8:

FAQs

What is ETL?

ETL, an acronym for Extract, Transform, Load, is a vital data integration process. It involves extracting data from diverse sources, transforming it into a usable format, and loading it into a database, data warehouse or data lake. This process enables meaningful data analysis, enhancing business intelligence.

What is Elasticsearch?

Elasticsearch is a distributed search and analytics engine for all types of data. Elasticsearch is the central component of the ELK Stack (Elasticsearch, Logstash, and Kibana).

What data can you extract from Elasticsearch?

Elasticsearch's API provides access to a wide range of data types, including:
1. Textual data: Elasticsearch can index and search through large volumes of textual data, including documents, emails, and web pages.
2. Numeric data: Elasticsearch can store and search through numeric data, including integers, floats, and dates.
3. Geospatial data: Elasticsearch can store and search through geospatial data, including latitude and longitude coordinates.
4. Structured data: Elasticsearch can store and search through structured data, including JSON, XML, and CSV files.
5. Unstructured data: Elasticsearch can store and search through unstructured data, including images, videos, and audio files.
6. Log data: Elasticsearch can store and search through log data, including server logs, application logs, and system logs.
7. Metrics data: Elasticsearch can store and search through metrics data, including performance metrics, network metrics, and system metrics.
8. Machine learning data: Elasticsearch can store and search through machine learning data, including training data, model data, and prediction data.

Overall, Elasticsearch's API provides access to a wide range of data types, making it a powerful tool for data analysis and search.

How do I transfer data from Elasticsearch?

This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:
1. Set up Elasticsearch to MSSQL - SQL Server as a source connector (using Auth, or usually an API key)
2. Choose a destination (more than 50 available destination databases, data warehouses or lakes) to sync data too and set it up as a destination connector
3. Define which data you want to transfer from Elasticsearch to MSSQL - SQL Server and how frequently
You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

What is ELT?

ELT, standing for Extract, Load, Transform, is a modern take on the traditional ETL data integration process. In ELT, data is first extracted from various sources, loaded directly into a data warehouse, and then transformed. This approach enhances data processing speed, analytical flexibility and autonomy.

Difference between ETL and ELT?

ETL and ELT are critical data integration strategies with key differences. ETL (Extract, Transform, Load) transforms data before loading, ideal for structured data. In contrast, ELT (Extract, Load, Transform) loads data before transformation, perfect for processing large, diverse data sets in modern data warehouses. ELT is becoming the new standard as it offers a lot more flexibility and autonomy to data analysts.

Back to all tutorials

Databases

How to load data from Elasticsearch to MS SQL Server

Learn how to use Airbyte to synchronize your Elasticsearch data into MS SQL Server within minutes.

Try Airbyte Cloud free

Deploy Airbyte Open Source

TL;DR

What is Elasticsearch

What is MS SQL Server

Prerequisites

Step 1: Setup Elasticsearch

Step 2: Setup MS SQL Server

Step 3: Setup a connection Use Cases Wrapping Up

Example H2

Example H3

Sync data between Elasticsearch and MS SQL Server

Get your data syncing in minutes

Try Airbyte free

TL;DR

set up Elasticsearch as a source connector (using Auth, or usually an API key)
set up MS SQL Server as a destination connector
define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Elasticsearch

Elasticsearch is a distributed search and analytics engine for all types of data. Elasticsearch is the central component of the ELK Stack (Elasticsearch, Logstash, and Kibana).

What is MS SQL Server

Integrate Elasticsearch with MS SQL Server in minutes

Try for free now

Prerequisites

A Elasticsearch account to transfer your customer data automatically from.
A MS SQL Server account.
An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Step 1: Set up Elasticsearch as a source connector

Step 2: Set up MS SQL Server as a destination connector

Step 3: Set up a connection to sync your Elasticsearch data to MS SQL Server

Once you've successfully connected Elasticsearch as a data source and MS SQL Server as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
Choose your source: Select Elasticsearch from the dropdown list of your configured sources.
Select your destination: Choose MS SQL Server from the dropdown list of your configured destinations.
Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
Select the data to sync: Choose the specific Elasticsearch objects you want to import data from towards MS SQL Server. You can sync all data or select specific tables and fields.
Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Elasticsearch to MS SQL Server according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your MS SQL Server data warehouse is always up-to-date with your Elasticsearch data.

Use Cases to transfer your Elasticsearch data to MS SQL Server

Integrating data from Elasticsearch to MS SQL Server provides several benefits. Here are a few use cases:

Advanced Analytics: MS SQL Server’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Elasticsearch data, extracting insights that wouldn't be possible within Elasticsearch alone.
Data Consolidation: If you're using multiple other sources along with Elasticsearch, syncing to MS SQL Server allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
Historical Data Analysis: Elasticsearch has limits on historical data. Syncing data to MS SQL Server allows for long-term data retention and analysis of historical trends over time.
Data Security and Compliance: MS SQL Server provides robust data security features. Syncing Elasticsearch data to MS SQL Server ensures your data is secured and allows for advanced data governance and compliance management.
Scalability: MS SQL Server can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Elasticsearch data.
Data Science and Machine Learning: By having Elasticsearch data in MS SQL Server, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
Reporting and Visualization: While Elasticsearch provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to MS SQL Server, providing more advanced business intelligence options. If you have a Elasticsearch table that needs to be converted to a MS SQL Server table, Airbyte can do that automatically.