Top 21 ETL Tools For 2023

In this digital world, everyday great volumes of data are being generated from varied sources, and companies want to give this data a form and structure by assembling it in an organized way and at a unified place to use it to their advantage. They want to analyze their data further to get a good understanding and to make well-informed data-driven decisions. To bring meaning to this raw data, ETL tools play a significant role and help businesses to take data analytics to the next level.

There are several ETL tools available in the market which automate this whole process of building data-pipeline, managing and monitoring them. In this article, we will understand the whole process of ETL in detail and ETL tools that are best suited to automate your data pipelines for accurate analysis.

What is ETL?‍

ETL stands for “Extract, Transform and Load”. ETL is a process of extracting data from different data sources, cleansing and organizing it, and eventually, loading it to a target data warehouse or a Unified data repository.

Why ETL?

In today's data-centric world ETL plays a vital role in maintaining the integrity of a company by keeping its data up to date. To get the correct insight it is therefore important to perform ETL mainly due to the following reasons:

1. Data Volumes: The generated data has very high volume and velocity as many organizations have historical as well as real-time data being forged continuously from different sources.

2. Data Quality: The quality of the generated data is not exemplary as data is present in different formats like online feeds, online transactions, tables, images, excel, CSV, JSON, text files, etc. Data can be structured or unstructured, so to bring all different data formats to one homogeneous format performing the ETL process is highly needed.

 

To overcome these challenges many ETL tools are developed that make this process easy and efficient and help organizations combine their data by going through processes like de-duplicating, sorting, filtering, merging, reformatting, and transforming to make data ready for analysis.

ETL in detail:

1. Extract:

Extract is the first step of the ETL process that involves data being pulled from different data sources. It can extract data from the following sources listed below - 

·         Data Storage Platform & Data warehouses

·         Analytics tool

·         On-premise environment, hybrid, and cloud

·         CRM and ERP systems

·         Flat files, Email, and Web Pages

Manual data extraction can be highly time-consuming and error-prone, so to overcome these challenges automation of the Extraction process is the optimal solution.

Data Extraction: Different ways of extracting data.

1.1. Notification-based

In Notification-based extraction whenever data is updated, a notification is generated either through data replication or through webhooks (SaaS application). As soon as the notification is spawned data is pulled from the source. It is one of the easiest ways to detect any update but is not doable for some data sources that may not support generating a notification.

1.2. Incremental Extraction

In incremental extraction, only records that have been altered or updated are extracted/ingested. This extraction is majorly preferred for daily data ingestion as low-volume data is transferred making the daily data extraction process efficient. One major drawback of this extraction technique is once the extracted data is deleted it may not be detected.

 

1.3. Complete data extraction

In complete data extraction, the entire data is loaded. If a user wants to get full data or to ingest data for the first time then complete data extraction is preferred. The problem with this type of extraction is that if the data volume is massive it can be highly time-consuming.

Challenges in Data Extraction:

Data extraction is the first and foremost step in the ETL process, so we need to ensure the correctness of the extraction process before proceeding to the next step.

Data can be extracted using SQL or through API for SaaS, but this way may not be reliable as the API may change often or be poorly documented and different data sources can have various APIs. This is one of the major challenges faced during the data extraction process, other challenges are mentioned below.

·         Changing data formats

·         Increasing data volumes

·         Updates in source credentials.

·         Data issue with Null values

·         Change requests for new columns, dimensions, derivatives, and features.

2. TRANSFORM

Transform is the second step of the ETL process, in this raw data undergoes processing and modifications in the staging area. In this process, data is shaped according to the business use case and the business requirements.

The transformation layer consists of some of the following steps:

·         Removing duplicates, cleaning, filtering, sorting, validating, and affirming data.

·         Data inconsistencies and missing values are determined and terminated.

·         Data encryption or data protection as per industrial and government rules is implemented for security.

·         Formatting regulations are applied to match the schema of the target data repository

·         Unused data and anomalies are removed

Data Transformation: Different ways of transforming data

2.1. Multistage Data Transformation –

In multistage data transformation, data is moved to an intermediate area or staging area where all the transformation steps take place then eventually data is transferred to the final data warehouse where the business use cases are implemented for better decision-making.

2.2. In-Warehouse Data Transformation –

In ‘In-Warehouse Data Transformation ’,‍ data is first loaded into the data warehouse, and then all the subsequent data transformation steps are performed. This approach of transforming data is followed in the ELT process.

Challenges in Data Transformation

Data transformation is the most vital phase of the ETL process as it enhances data quality and guarantees data integrity yet there are some challenges faced when transforming data comes into play. Some challenges faced in transforming data are mentioned below:

·         Increasing data volumes makes it difficult to manage data and any transformation made can result in some data loss if not done properly.

·         The data transformation process is quite time-consuming and the chances of errors are also very high due to the manual effort.

·         More manpower and skills are required to efficiently perform the data transformation process which may even lead businesses to spend high.

3. LOAD

Once data is transformed, it is moved from the staging area to the target data warehouse which could be on the cloud or on-premise. Initially, the entire data is loaded, and then recurring loading of incremental data occurs. Sometimes, a full fetch of data takes place in the data warehouse to erase and replace old data with new one to overcome data inconsistencies.

Once data is loaded, it is optimized and aggregated to improve performance. The end goal is to quicken up the query span for the analytics team to perform accurate analysis in no time.

Data Loading: Considerations for error-free loading

·         Referential integrity constraint needs to be addressed effectively when new rows are inserted or a foreign key column is updated.

·         Partitions should be handled effectively for saving costs on data querying.

·         Indexes should be cleared before loading data into the target and rebuilt after data is loaded.

·         In Incremental loading, data should be in synchronization with the source system to avoid data ingestion failures.

·         Monitoring should be in place while loading the data so that any data loss creates warning alerts or notifications.

Challenges in Data Loading:

Data loading is the final step of the ETL process. This phase of ETL is responsible for the execution of correct data analysis. Therefore one must ensure that the data quality is up to the mark. The main challenge faced during data loading is mentioned below:

Data loss – While loading the data into the target system, there might be API unavailability, network congestion/failure or API credentials may expire these factors can result in complete data loss posing a greater threat to the business.

Overall Challenges of ETL

1. Code Issues

If ETL pipeline code is not optimized or manually coded, then such inefficiencies might affect the ETL process at any stage: It may cause problems while extracting data from the source, transforming data, or loading data into the target data warehouse and backtracking the issue can even be a tedious task.

2. Network Issues

The ETL process involves massive data transfer and processing on a daily basis which needs to be quick and efficient. So, the network needs to be fast and reliable, high latency of the network may create unexpected troubles in any of the stages and any network outage may even lead to data loss.

3. Lack of resources

Lack of any computing resources including storage, slow downloading, or lagging data processing in ETL may lead to fragmentation of your file system or create caches over a period of time.

4. Data Integrity

Since ETL involves collecting data from more than one source, if not done rightly, data might get corrupted which may create several inconsistencies and hence can cause data health reduction. So latest data needs to be carefully collected from sources and transformation techniques should be used accordingly.

5. Maintenance

In any organization increase in data corresponds to an increase in data sources so for business to maintain all their enormous data in a unified place more data connectors will keep on adding. So, while planning the ETL process, scalability, maintenance and the cost of maintenance should always be considered.

ETL vs ELT?

The main difference between ETL and ELT is the order of transformation, in ETL it happens before loading the data into the data warehouse however in ELT, data is first loaded and then its transformation takes place in the warehouse itself.

ELT Benefits over ETL

·         When dealing with high volumes of data ELT has a better advantage over ETL as transforming data before loading it into the data warehouse is an error-prone process and any mistake during transformation can cause complete data loss. Whereas in ELT data is first loaded into the warehouse and then it is transformed. So the chances of data loss are minimized in ELT as the data sits in the warehouse itself.

·         In ELT, not much planning is required by the team as compared to the ETL process. In ETL proper transformation rules need to be identified before the data loading process is executed which can be very time-consuming.

·         ELT is ideal for big data management systems and is adopted by organizations making use of cloud technologies, which is considered an ideal option for efficient querying.

·         For ETL, the process of data ingestion is very slow and inefficient, as the first data transformation takes place on a separate server, and after that data loading process starts. ELT does much faster data ingestion, as there is no data transfer to a secondary server for any restructuring. In fact, with ELT data can be loaded and transformed simultaneously.

·         ELT as compared to ETL is much faster, scalable, flexible, and efficient for large datasets which consist of both structured and unstructured data. ELT also helps to save data egress costs as before the transformation process the data sits in the data warehouse only.

Why do we need ETL tools?

ETL tools help to make the ETL process fast and efficient hence benefitting the businesses to stay one step ahead of their competitors. Some of the benefits of choosing the right ETL tool for your business are mentioned below:

1. Time Efficient: ETL tools permit us to collect, modify and integrate data automatically. Using ETL tools businesses can save more time as compared to time spent by bringing in data physically.

2. Low-code analysis: ETL tools generally offer low code/ no code functionality which helps to boost efficiency, requires less manual effort, and helps in keeping costs at bay.

3. Analyzing & Reporting: With the introduction of ETL tools analyzing data for reporting and dashboarding has become very easy. Data is available to us in a consolidated view and with the help of the right ETL tools, accurate analysis and reporting can be done.

4. Historical context: Businesses can benefit by analyzing the historical trends of their data to get its deep historical context and to predict some upcoming trends. There are many ETL tools available in the market that can efficiently analyze historical data in no time.

5. Data governance and ROI: It improves data accuracy and audit which is required for compliance with regulations and standards. It results in higher ROI for investments made in data teams.

There are numerous other benefits of ETL tools but the main challenge is to identify which ETL tool should be used by organizations to implement the right business use case according to their requirements.

Criteria for Choosing the Right ETL Tools

With the emergence of modern data-driven businesses, the space of ETL tools have also seen huge interest making the zone a crowded sector. But, with so many ETL Tools available in the market how should we go ahead with choosing the right ETL Tools for our businesses?

Below listed are some criteria that one should consider before choosing the right ETL tool according to the requirements.

1. In-built Connectors:

ETL tools with a high number of connectors should be preferred as they will provide more flexibility to businesses. Not only connectors but also, some of the widely used databases and applications in the industry must be available in the ETL tool that we choose.

2. Ease of Use and Clean User Interface:

ETL tools should be user-friendly, and an easy-to-understand user interface saves a lot of time and effort for its users and help them in using the tool hassle-free. Along with this, clear documentation to users should also be provided to get a better understanding of the selected ETL tool.

3. Scalable:

With the emergence of data-centric businesses, data tends to grow exponentially every day so to keep up with the cost a scalable ETL tool is of paramount importance for every business. We must choose an ETL tool with a good scalability option available to cater to business needs.

4. Error Handling:

Data Consistency and accuracy should be at the helm of any ETL tool that we choose for our business. In addition to this, the ETL tool should also have capabilities of smooth and efficient data transformation capabilities.

5. Real-time Data Ingestion and Monitoring:

ETL Tools with the capability to ingest data on a real-time basis from a wide range of sources should be highly considered for businesses that generate data every day. Apart from this, monitoring of the data ingestion and transformation process should be done accurately in that ETL tool to keep track of your data.

Check here Top 21 ETL Tools for 2023 : https://www.sprinkledata.com/blogs/etl-tools