Ultimate ETL Guide: 14 Comprehensive Aspects

• June 2nd, 2021


It is a common practice for most businesses today to rely on data-driven decision-making. Businesses collect a large volume of data and leverage it to perform an in-depth analysis of their customers and products, allowing them to plan future Growth, Product, and Marketing strategies accordingly. In order to perform a proper analysis, the first step is to create a Single Source of Truth for all your data.

This means that data has to be extracted from all platforms and stored in a centralized database. Considering the high volume of data that most businesses collect today, this becomes a complicated task and requires a well-designed data pipeline tool to effectively carry out the ETL process. ETL is short for extract, transform and load, where data engineers extract data from various sources, transform and load that data into the systems where stakeholders can access actionable insights to solve business problems.

This article will provide you with a comprehensive understanding of what ETL is, how you can create a simple data pipeline using Python, and the best data pipeline tools you can leverage to make your data transfer more accurate and efficient.

Table of Contents

What is ETL?

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ETL can be defined as a data integration process divided into three steps, i.e., extract, transform and load. It is primarily used to integrate data from multiple sources and load it in a centralized location, typically a Data Warehouse for analytical purposes. During this process, necessary data is extracted from all data sources, transformed into a form suitable for analysis, and loaded into a destination based on the requirement. ELT is an alternate but related approach designed to push processing down to the destination for improved performance.

The three main steps of the ETL process are as follows:

  • Extract: Extraction can be defined as the process of gathering all essential data from the source systems. For most ETL processes, these sources can be Databases such as MySQL, MongoDB, Oracle, etc., Customer Relationship Management (CRM), Enterprise Resource Planning (ERP) tools, or various other files, documents, web pages, etc.
  • Transform: Transformation can be defined as the process of converting the data into a format suitable for analysis such that it can easily be understood by a Business Intelligence or Data Analysis tool. The following operations are usually performed in this phase:
    • Filtering, de-duplicating, cleansing, validating, and authenticating the data.
    • Performing all necessary translations, calculations, or summarizations on the extracted raw data. This can include operations such as changing row and column headers for consistency, standardizing data types, and many others to suit the organization’s specific Business Intelligence (BI) and Data Analysis requirements.
    • Encrypting, removing, or hiding data governed by industry or government regulations.
    • Formatting the data into tables and performing the necessary joins to match the Schema of the destination Data Warehouse.
  • Load: Loading can be defined as the process of storing the transformed data in the destination of choice, normally a Data Warehouse such as Amazon Redshift, Google BigQuery, Snowflake, etc.

You can also read our article about the best tools for Oracle ETL.

ETL Versus ELT

Traditional ETL processes extract and transform data before they are loaded into the Data Warehouse. However, most businesses now leverage Cloud-based Data Warehouses to store all their operational data for analytical purposes rather than setting up their own On-premise Data Warehouse. Although businesses can still use the traditional ETL process for Cloud-based systems, it is not considered ideal anymore, and ELT is preferred over ETL.

In terms of workload and data management, Cloud-based systems are much more scalable in terms of processing and storage than traditional On-premise Data Warehouses. The ETL process is unlikely to take advantage of the numerous improvements that a Cloud-based Data Warehouse offers. The ETL process treats Cloud-based Data Warehouses like traditional Data Warehouses. Hence, this results in the same set of performance bottlenecks, and therefore, the switch to Cloud-based systems provides no added value. 

ELT (Extract, Load, Transform) is built to leverage all the best features of a Cloud-based Data Warehouse, such as Massively Parallel Processing, elastic scalability, and the ability to spin up and tear down jobs quickly. This means that all necessary data is extracted from the sources and loaded into the Cloud without any transformations. The high processing power of the Cloud is then leveraged to perform the necessary transformations on the data as and when it is required.

However, the ELT processes would require much more storage space than ETL since the raw data would have to be stored without any transformations. This means that the necessary data would have to be extracted from the raw data storage and transformed every time it is required for analysis.

Setting up a Simple ETL Process using Python

This section will help you understand how you can set up a simple data pipeline that extracts data from MySQL, Microsoft SQL Server, and Firebird databases and loads it into a Microsoft SQL Server database. It will also give you a basic idea of how easy it is to set up an ETL process using Python.

The same process can also be used to implement a custom script based on your requirements by making changes to the databases being used and queries accordingly. In order to set up an ETL process using Python, the following steps have to be implemented:

Step 1: Installing Required Modules

The following Modules are required to set up an ETL process using Python for the above mentioned data sources:

The Pip command can be used to install the required modules. These Modules can be installed by running the following commands in the Command Prompt:

pip install mysql-connector-python
pip install pyodbc
pip install fdb

Step 2: Setting up ETL Directory

The following files should be created in your project directory in order to set up ETL Using Python:

  • db_credentials.py: It should contain the data required to establish connections with all databases. For example, Database Password, Port Number, etc.
  • sql_queries.py: It should have all the commonly used database queries to extract, and load data in String format.
  • etl.py: It should perform all necessary operations to connect with the database and run the required queries.
  • main.py: It is responsible for maintaining the flow of the operations and performing the necessary operations in a specific order.

A) db_credentials.py

This file is required for setting up all Source and Target Database Connection Strings. This file should have the required information to access the appropriate database in a list format so that it can be iterated easily when required.

The format for this file is as follows:

datawarehouse_name = 'your_datawarehouse_name'

# sql-server (target db, datawarehouse)
datawarehouse_db_config = {
  'Trusted_Connection': 'yes',
  'driver': '{SQL Server}',
  'server': 'datawarehouse_sql_server',
  'database': '{}'.format(datawarehouse_name),
  'user': 'your_db_username',
  'password': 'your_db_password',
  'autocommit': True,

# sql-server (source db)
sqlserver_db_config = [
    'Trusted_Connection': 'yes',
    'driver': '{SQL Server}',
    'server': 'your_sql_server',
    'database': 'db1',
    'user': 'your_db_username',
    'password': 'your_db_password',
    'autocommit': True,

# mysql (source db)
mysql_db_config = [
    'user': 'your_user_1',
    'password': 'your_password_1',
    'host': 'db_connection_string_1',
    'database': 'db_1',
    'user': 'your_user_2',
    'password': 'your_password_2',
    'host': 'db_connection_string_2',
    'database': 'db_2',

# firebird (source db)
fdb_db_config = [
    'dsn': "/your/path/to/source.db",
    'user': "your_username",
    'password': "your_password",

B) sql_queries.py

This file contains queries that can be used to perform the required operations to extract data from the Source Databases and load it into the Target Database.

The format for this file is as follows:

# example queries, will be different across different db platform
firebird_extract = ('''
  SELECT fbd_column_1, fbd_column_2, fbd_column_3
  FROM fbd_table;

firebird_insert = ('''
  INSERT INTO table (column_1, column_2, column_3)
  VALUES (?, ?, ?)  

firebird_extract_2 = ('''
  SELECT fbd_column_1, fbd_column_2, fbd_column_3
  FROM fbd_table_2;

firebird_insert_2 = ('''
  INSERT INTO table_2 (column_1, column_2, column_3)
  VALUES (?, ?, ?)  

sqlserver_extract = ('''
  SELECT sqlserver_column_1, sqlserver_column_2, sqlserver_column_3
  FROM sqlserver_table

sqlserver_insert = ('''
  INSERT INTO table (column_1, column_2, column_3)
  VALUES (?, ?, ?)  

mysql_extract = ('''
  SELECT mysql_column_1, mysql_column_2, mysql_column_3
  FROM mysql_table

mysql_insert = ('''
  INSERT INTO table (column_1, column_2, column_3)
  VALUES (?, ?, ?)  

# exporting queries
class SqlQuery:
  def __init__(self, extract_query, load_query):
    self.extract_query = extract_query
    self.load_query = load_query
# create instances for SqlQuery class
fbd_query = SqlQuery(firebird_extract, firebird_insert)
fbd_query_2 = SqlQuery(firebird_extract_2, firebird_insert_2)
sqlserver_query = SqlQuery(sqlserver_extract, sqlserver_insert)
mysql_query = SqlQuery(mysql_extract, mysql_insert)

# store as list for iteration
fbd_queries = [fbdquery, fbd_query_2]
sqlserver_queries = [sqlserver_query]
mysql_queries = [mysql_query]

C) etl.py

This file should contain the code that helps establish connections among the correct databases and run the required queries.

The format for this file is as follows:

# python modules
import mysql.connector
import pyodbc
import fdb
def etl(query, source_cnx, target_cnx):
  # extract data from source db
  source_cursor = source_cnx.cursor()
  data = source_cursor.fetchall()

  # load data into warehouse db
  if data:
    target_cursor = target_cnx.cursor()
    target_cursor.execute("USE {}".format(datawarehouse_name))
    target_cursor.executemany(query.load_query, data)
    print('data loaded to warehouse db')
    print('data is empty')

def etl_process(queries, target_cnx, source_db_config, db_platform):
  # establish source db connection
  if db_platform == 'mysql':
    source_cnx = mysql.connector.connect(**source_db_config)
  elif db_platform == 'sqlserver':
    source_cnx = pyodbc.connect(**source_db_config)
  elif db_platform == 'firebird':
    source_cnx = fdb.connect(**source_db_config)
    return 'Error! unrecognised db platform'
  # loop through sql queries
  for query in queries:
    etl(query, source_cnx, target_cnx)
  # close the source db connection

D) main.py

The code in this file is responsible for iterating through credentials to connect with the database and perform the required ETL operations.

The format for this file is as follows:

# variables
from db_credentials import datawarehouse_db_config, sqlserver_db_config, mysql_db_config, fbd_db_config
from sql_queries import fbd_queries, sqlserver_queries, mysql_queries

# methods
from etl import etl_process

def main():
  print('starting etl')
  # establish connection for target database (sql-server)
  target_cnx = pyodbc.connect(**datawarehouse_db_config)
  # loop through credentials

  # mysql
  for config in mysql_db_config: 
      print("loading db: " + config['database'])
      etl_process(mysql_queries, target_cnx, config, 'mysql')
    except Exception as error:
      print("etl for {} has error".format(config['database']))
      print('error message: {}'.format(error))
  # sql-server
  for config in sqlserver_db_config: 
      print("loading db: " + config['database'])
      etl_process(sqlserver_queries, target_cnx, config, 'sqlserver')
    except Exception as error:
      print("etl for {} has error".format(config['database']))
      print('error message: {}'.format(error))

  # firebird
  for config in fbd_db_config: 
      print("loading db: " + config['database'])
      etl_process(fbd_queries, target_cnx, config, 'firebird')
    except Exception as error:
      print("etl for {} has error".format(config['database']))
      print('error message: {}'.format(error))

if __name__ == "__main__":

Although this process can be used to implement simple ETL processes using Python, it might lead to performance issues if the data sources generate complex Schemas or return a high volume of data. To deal with these use cases, users can implement ETL tools that are built to handle high volumes of data with complex Schemas.

How is ETL being Used?

ETL can come in handy for Data Management tasks in collaboration with multiple tools. Here are a few ways ETL is being utilized:

  • Big Data: Traditional transactional and operational data makes up only a small fraction of the data being accumulated across companies today. The amount of Big Data flowing into companies from log mining, video, social media, IoT, and more is huge. This voluminous data allows businesses to gain a competitive edge, make accurate decisions, and gain context regarding their customers. ETL vendors need to constantly update the connectors and transformation capability to evolve as data sources and data types multiply and evolve.
  • Hadoop: Companies are making the shift from traditional ETL tools, Data Warehouses, and structured master data to Hadoop. Hadoop provides an environment that allows data engineers to increase the scalability and speed of their processes. A centralized Hadoop repository has the upper hand over traditional data warehousing since it can scale more easily. On top of this, Hadoop is an open-source tool and a viable low-cost alternative to traditional data technology.
  • Traditional Use Cases: Most fundamentally these tools help businesses combine unstructured and structured data accumulated from source systems and land them in a Data Warehouse. The raw data is often manipulated into table structures optimized for reporting. This allows hidden insights to surface through visualization or analytics software. It also allows businesses to migrate data from legacy warehouses to the cloud and consolidate data from two different entities.

How is ETL Commonly Used?

There are various common use cases of ETL as mentioned below:

  • Cloud Migration: Since its inception, Cloud Computing has helped businesses migrate data from data to the cloud to extract actionable and valuable insights from it. Cloud-native tools use the advantage of the cloud, to load data directly to the cloud and transform it within the cloud infrastructure. This allows Data professionals to save money and time.
  • Machine Learning and AI: Many companies have begun exploring the impact of AI and Machine Learning in Data Science and Analytics. So far, the cloud is the only practical solution for large-scale AI and Machine Learning operations. Both techniques need large datastores for analytical training and model building, as well as for automated Data Analysis. Cloud-based tools hold the key to migrating large amounts of data to the cloud.
  • Data Warehousing: Companies have traditionally used ETL to accumulate data from various sources, transform it into an analytics-ready, consistent format and load it to a Data Warehouse. This allows business teams to analyze it for business purposes.
  • Marketing Data Integration: Customers interact with businesses on multiple channels today, which can make it difficult for marketers to monitor their activity across all channels to understand their behavior. These tools can be critical to integrating and collecting customer data from eCommerce, websites, Social Networking, mobile applications, and other platforms. It also allows you to integrate other contextual data so marketers can apply hyper-personalization, offer incentives, improve user experience, and more.

How ETL works?

Traditionally this process will extract data from one or multiple OLTP (Online Transactional Processing) databases. OLTP applications contain a high volume of transactional data that needs integration and transformation to operational data. This is required because it can be helpful for Business Intelligence and Data Analysis.

This data is extracted into a staging area, a storage location that sits between the data source and the data target. In that staging area, these tools can transform data by joining, cleansing, and otherwise optimizing it for analysis.

This tool can then load this data into a Decision Support System (DSS) database, where BI teams can run queries and depict reports and results to business users to help them make decisions and set strategies.

But since the traditional tools still require a fair amount of labor from data professionals, this is where modern tools come in. The Cloud has changed forever with ELT. Nowadays, powerful Cloud Data Warehouses such as Google BigQuery, Snowflake, and Amazon Redshift don’t need external resources to perform transformations.

Data can easily be analyzed from pre-calculated OLAP summaries, which helps simplify and speed up the process.

What is Self-Service Data Access?

Self-service Data Preparation allows business users and non-technical data professionals to extract, transform, load, and run business operations by themselves. Decision-makers no longer have to wait for Data professionals to provide data for data analysts. Data Analysts who self-serve provide the flexibility to pull critical data any time you need it. This also allows businesses to spend less time on Data Preparation and spend more time fostering innovation and deriving insights. Here are a few ways this is beneficial:

  • Metadata: Metadata tells you about where data comes from. An ETL process, not only extracts source data but also collects metadata that is critical for the target system to have when doing business intelligence activities like reporting, data modeling, etc. This allows developers to create data structures only once in the repository after which it lives on over there. The metadata is stored in a metadata repository from which it can be queried, manipulated, and retrieved.
  • Data Quality: ETL Tools can improve the quality of data. The ability to automate and standardize the processes that move from a source system into the Data Warehouse means that you’re less likely to have messy data. Since messy data can lead to inaccurate analysis and insights, these tools are integrated with data quality tools. It includes features for data-quality related functions such as data lineage and data mapping.

IoT (Internet of Things) Data Integration

IoT is a fast-growing source of data for businesses. This can range from embedded sensors to wearable devices. Here are a few ways IoT can easily integrate with ETL processes to give you the best output when it comes to a seamless data migration process:

  • Business Intelligence: Companies need to analyze data to provide business intelligence that allows stakeholders and managers to make informed decisions. Therefore, they need to be based on an organization’s data. The Cloud Data Warehouse is becoming a crucial element for Business Intelligence and Data Analytics, which makes it critical for information management and faster time to gather insights.
  • Data Replication: ETL is also critical to Database Replication, irrespective of whether you are moving data from source databases as an ongoing process or as a one-time operation. This involves moving data from an on-premises data warehouse to a cloud data warehouse. However, as more enterprises move to the cloud, it can mean moving from one cloud service provider or infrastructure to other.

What are the Common Types of ETL Tools?

Here are the common types of ETL tools you can find in the marketplace:

  • Cloud-Native ETL Tools: Data is now moving into the cloud and so is ETL. Various cloud-native applications have cropped up that can extract and load data from sources directly into a Cloud Data Warehouse. They can then easily transform data by using the power of the cloud. They can be deployed directly into your cloud infrastructure, or hosted in the cloud as a SaaS.
  • Batch Processing ETL Tools: Until some time ago, Batch Processing was the only way to do ETL for a lot of businesses. Since processing large volumes of data took a lot of resources and time it could easily exhaust a company’s storage and compute power during business hours. Therefore, it made more sense back then to batch process this data during off-hours.
  • Real-Time ETL Tools: The increasing demand for real-time data requires that we process data in real-time. This includes a distributed model and streaming capabilities as opposed to the Batch Processing tools. There are various Real-time tools available both open-source and commercially packaged. Check our guide on streaming ETL.
  • Open-Source ETL Tools: These tools are a low-cost alternative to commercially packaged solutions, and are a practical option for many businesses. Some common Open-source tools are Apache Kafka, Apache Nifi, and Apache Airflow. One downside to Open-source tools is that they aren’t designed to handle the data complexities faced by modern enterprises, and may lack support for desirable features and complex data transformation.

6 Best ETL Tools

The 6 best ETL tools available in the market are as follows:

We have also written an article about top ETL reporting tools. You’ll find more tools in that article.

1) Hevo

Hevo Logo

Hevo is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 100+ data sources (including 30+ free data sources) and will let you directly load data to a Data Warehouse or the destination of your choice. It will automate your data flow in minutes without writing any line of code.

Get Started with Hevo for Free

Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully automated solution to manage data in real-time and always have analysis-ready data. Hevo also gives users the ability to set up an ETL process that allows them to load data from a Data Warehouse of their choice to applications such as HubSpot, Salesforce, etc. using its Activate offering.

Let’s Look at Some Salient Features of Hevo:

  • Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled in a secure, consistent manner with zero data loss.
  • Schema Management: Hevo takes away the tedious task of schema management & automatically detects schema of incoming data and maps it to the destination schema.
  • Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
  • Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
  • Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
  • Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
  • Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Sign up here for a 14-Day Free Trial!

Hevo Pricing

Hevo offers two paid tiers, i.e., Starter and Business, along with its Free tier. The pricing for each paid tier depends on the number of events a user is expected to integrate. The Starter tier offers 20 Million events at $299/month, 50 Million events at $499/month, 100 Million events at $749/month, 200 Million events at $999/month, and 300 Million events at $1249/month. The Business tier is a custom tier for large Enterprises with complex requirements. Users can schedule a call with the Hevo team to create a tailor-made plan in the Business tier based on their unique requirements.

Hevo Pricing
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An in-depth comparison of the features offered in each tier are as follows:

Hevo Tiers Comparison
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More details on Hevo can be found here, and its pricing can be found here.

2) Informatica PowerCenter

Informatica Logo
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Informatica PowerCenter is an Enterprise-grade data integration platform. It gives users the ability to integrate data from various data sources, including numerous SQL and NoSQL databases. Informatica PowerCenter’s data integration platform is considered to be highly scalable. Hence, it scales up or down as the business requirement changes and helps transform fragmented data into an analysis-ready form. It is capable of delivering data on-demand, i.e., real-time and data capturing. Informatica PowerCenter is best suited for large Enterprises that wish to perform complex operations such as advanced transformation, dynamic partitioning, data masking, etc.

Informatica PowerCenter Pricing

Informatica offers a 30-day free trial for its products, following which the users have to start paying. However, Informatica does not follow a transparent pricing model for its product, and the final price depends on your unique business and data requirements.

Informatica Pricing
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More information about Informatica PowerCenter can be found here and its pricing can be found here.

3) Xplenty

Xplenty Logo
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Xplenty is another popular Cloud-based data integration platform. It houses an intuitive and easy-to-use Graphical User Interface (GUI) that allows users to create automated data pipelines seamlessly. Xplenty gives users the ability to create and visualize their data pipelines from their workspaces and manage workflows easily using its drag-drop functionality.

Xplenty houses a diverse set of in-built connectors that allow users to integrate data from numerous data sources like Databases such as Oracle, MySQL, MongoDB, etc., Cloud Storage platforms such as Amazon S3, Google Cloud Storage, etc., Advertising tools such as LinkedIn Ads, Facebook Ads, etc. and load it in a destination of their choice such as Google BigQuery, Snowflake, etc.

Xplenty Pricing

Xplenty offers a 14-day free trial following which users have to start paying. However, Xplenty does not offer a transparent pricing model. Users can get in touch with the Xplenty team for more information on the product pricing.

More information about Xplenty can be found here and its pricing can be found here.

4) Stitch Data

Stitch Logo
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Stitch Data is seen as a more complex and detailed version of ETL. It is a Cloud-based Open source platform designed for processing rapidly moving data. Stitch Data is a powerful service that supports direct integration with a wide variety of data sources, including databases like MySQL, MongoDB, etc., Software-as-a-Service (SaaS) applications like Salesforce, Zendesk, etc. The data from these sources can be replicated across a wide variety of destinations, including Data Warehouses, Data Lakes, and storage platforms such as Google BigQuery, Amazon Redshift, Microsoft Azure Synapse Analytics, etc.

All the operations critical to pipeline creation are handled automatically by Stitch Data, allowing users to focus their efforts on building dashboards and deriving valuable insights rather than developing and maintaining data pipelines.

Stitch Data Pricing

Stitch Data offers two paid tiers, i.e., Standard and Enterprise. The pricing for each paid tier depends on the number of rows a user is expected to integrate. The Standard tier offers 5 Million rows at $100/month, 10 Million rows at $180/month, 25 Million rows at $350/month, 50 Million rows at $500/month, 100 Million rows at $750/month, 200 Million rows at $1000/month, and 300 Million rows at $1250/month. The Enterprise tier is a custom tier for large Enterprises with complex requirements. Users can schedule a call with the Stitch Data team to create a specific plan in the Enterprise tier based on their unique requirements.

Stitch Pricing
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An in-depth comparison of the features offered in each tier are as follows:

Stitch Tier Comparison
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More details on Stitch Data can be found here, and its pricing can be found here.

5) Fivetran

Fivetran logo
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Fivetran is one of the most popular ETL tools and is used by some of the largest organizations across the world. Fivetran allows for efficient integration of business processes and customer data from numerous related data sources. The data collected can then be transferred to various destinations for Marketing, Analytics, or Data Warehousing purposes. It offers its customers ready-to-use connectors that automatically adapt as the APIs and Schemas change, thereby ensuring consistency and reliable data acces. In three simple steps, Fivetran pushes data towards productive analysis and maximizes the scope for fetching better insights into data spread across various systems.

Fivetran Pricing

Fivetran offers a 14-day free trial, following which the users have to select one of the three tiers, i.e., Starter, Standard, and Enterprise, and pay based on a consumption pricing model. The pricing for each tier and an in-depth comparison of the features offered in each tier is as follows:

Fivetran Pricing
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More information about Fivetran can be found here, and its pricing can be found here.

6) Talend Open Studio

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Talend Open Studio is a popular free and Open-source ETL tool for Data Integration, Big Data, Data Profiling, Cloud Integration, etc. Talend Open Studio gives users the ability to create data pipelines using its Graphical User Interface (GUI) by integrating seamlessly with numerous data sources such as Relational Database Management Systems (RDBMS), Software-as-a-Service (SaaS) Big Data ecosystems, Customer Relationship Management (CRM) tools, Dropbox, Microsoft Excel, etc.

Talend Open Studio allows users to create data pipelines in a few days or hours that would ideally take a few weeks or months; monitor and manage complex deployments easily, and integrate data from numerous sources to a single destination. Another significant advantage of using Talend Open Studio is that it is an Open-source tool. This means that users can make changes to the code of the application to fit their use cases.

More information about Talend Open Studio can be found here.

How to Build an ETL Strategy?

The best ETL practices that can be implemented to ensure optimal integration results are as follows:

1) Minimizing Data Input

Users should ensure that only the relevant data makes it into the ETL process. As the volume of data being processed decreases, the quality and speed at which the results are produced will improve.

Hence, if there are any redundant or irrelevant entries in a database, they should be cleaned up before the ETL process starts rather than being added to the process and then removed in the transformation phase.

2) Using Incremental Data Updates

The efficiency and speed of the ETL process can further be enhanced by using Incremental Data Updates. This means that only the new data is processed through the data pipeline, rather than deleting the entire dataset at the destination and starting the process again from scratch. Although the implementation of incremental data updates from scratch is a complex process, it will save a lot of time and effort when the ETL process is deployed in production.

3) Setting up Checkpoints

There is always a high chance of unexpected errors or failures occurring at any point in time during the data integration process. Hence, users should ensure that checkpoints are established in the ETL process so that the process can be resumed from a given checkpoint if an error occurs instead of starting the process from the beginning.

4) Logging and Monitoring

Users should ensure that proper logs are maintained while the ETL process is running so that they can efficiently resolve all data consistency and accuracy issues. ETL logs should typically have information related to the timing and length of data extraction, the number of records that were integrated, information about Exceptions that occurred during the data integration process, etc. These logs should be monitored regularly to introduce the necessary improvements to the data pipeline.

5) Using Staging Tables for High Volume Data

As the volume of data being processed by the ETL pipeline increases, it starts facing performance and quality issues due to increased load. The performance of the pipeline with high volume data can be improved by using Staging tables. Staging tables are volatile tables in which the results are loaded temporarily from one execution to the next and then emptied once the processing is done. These tables are usually leveraged to only store interim results and not as permanent storage.

More information about the best ETL practices can be found here.

Why ETL is Important?

Here are a few reasons stating the importance of ETL.

  • ETL has the potential to provide historical context for your organization when used along with the data at rest in the warehouse.
  • ETL creates an easy path for businesses to analyze and provide the data relevantly on their initiatives.
  • Using ETL you can move data in without any technical skills to write code snippets. It increases the productivity of your team.
  • ETL supports the upcoming interaction requirements.
  • ETL becomes one of the most essential tools for an organization with its data warehousing, reporting, and analytics tools.

What does the future hold for ETL?

Here is what the future holds for ETL:

  • Data Democratization: In the future, data would become more ubiquitous. Businesses want and need their employees to make data-driven decisions, which means centralizing employing and data tools that reduce manual processes to increase time for insight will gain steam. This also means that businesses would be requiring different kinds of tools for different use cases. Pipeline tools for business users, streaming and batch capabilities based on the demand for real-time information, and full data transformation capabilities in IT. As organizations become more self-service based, they will continue to gain a competitive edge over the competitors who refuse to adapt.
  • Exponential Data Growth: IoT data will continue to grow and play a crucial role in our lives. Based on the recent statistics, we will continue to outgrow traditional Data Warehouses and would need to move to the cloud. This increases the importance of cloud-native tools to manage, transform, and integrate the data in the cloud.
  • More Artificial Intelligence and Machine Learning: Preparing the data for artificial intelligence and machine learning will become a more critical use case since digital assistance and the next-best-action technologies continue to expand on a very large scale.

What are the Common ETL Challenges?

These are the challenges being faced by ETL:

  • Diverse Data Sources: Data is quickly growing in complexity and volume. One company could be handling diverse data from multiple data sources that include semi-structured and structured sources, flat files, streaming sources, etc. Some of this data can easily be transformed in batches, while for others streaming replication might be the best bet. Handling each type of data in the most practical and effective manner can pose an enormous challenge.
  • Scalability: Scalability is crucial to the functioning of a modern tool. The amount of data being accumulated by businesses is only going to grow. You might be moving data in batches now, but as your business grows, you might need to switch to streaming replication. This is where the cloud needs to come in.
  • Accurate Transformation of Data: Another key challenge is complete and accurate data transformation. Manual changes and coding or failure to test and plan before running an ETL job can sometimes introduce errors, including missing data, loading duplicates, and other issues. An ETL tool can reduce the need to hand-code and cut down on errors drastically. Data Accuracy testing can be used to spot duplicates and inconsistencies. Monitoring features can be used to identify instances where you are dealing with incompatible data types and other Data Management issues.


This article provided you with a comprehensive understanding of what ETL is, along with a list of the best ETL tools available in the market. It also helped you understand the best ETL practices that can ensure that your high volume of data is migrated to your destination accurately and in the expected form.

Most businesses today, however, have an extremely high volume of data with a very dynamic structure. Creating an ETL pipeline from scratch for such data is a complex process since businesses will have to utilize a high amount of resources to develop it and then ensure that it can keep up with the increased data volume and Schema variations. Businesses can instead use automated platforms like Hevo.

Visit our Website to Explore Hevo

Hevo helps you directly transfer data from a source of your choice to a Data Warehouse or desired destination in a fully automated and secure manner without having to write the code or export data repeatedly. It will make your life easier and make data migration hassle-free. It is User-Friendly, Reliable, and Secure.

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