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Modern Data + AI Integration:Strategies and Architectures

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Big data has evolved considerably over the decades. In an increasingly data-driven world, businesses recognize the usefulness of insights derived from data. Hence, there is now a demand for intelligent and efficient data transformation tools. In the past, only highly technical data engineers with access to on-premises data warehouses could conduct large-scale data transformations. Today, data transformation tools, cloud computing, and modern data governance models such as data mesh democratize the power of data.

As we will see in this article, modern data transformation tools don’t always require coding. They can be easy-to-use no-code solutions that allow non-technical users to design ETL (extract, transform, and load) pipelines – where ETL is the process of transforming source data into ready-to-use data.

This article will explore the modern data transformation tool features and use cases in detail. 

Summary of data transformation tools must-have features

The table below summarizes the data transformation tool concepts we will review in this article. 

Concept Description
Versioning, configuration, and audit logs Monitoring tools that keep track of data changes and processing rules over time which enable traceability and accountability for data quality  and consistency. 
Data lineage A complete history of how data has changed throughout each step of the transformation process.
Schema drift An indicator of how the schema or structure of a dataset differ.
Data drift An indicator of how the datasets differ.
Data monitoring Tools that highlight essential information and issues regarding data.
Cloud integration A feature that pertains to the ability of a data transformation tool to work in conjunction with cloud services.
No-code and low-code transformations No-code and low-code transformations enable users with diverse skill sets to manipulate and process data through user-friendly interfaces. 
Data characteristics Metrics can be derived from the data, including mean, standard deviation, and others.

Features of the ideal data transformation tools

Speed, accuracy, and ease of use are essential characteristics of the ideal data transformation tool. The sections below describe important data transformation tool features. 

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Support for different file formats while having a standard interim data format

Data can be structured, unstructured, or semi-structured. Additionally, they can be extracted from various data sources, such as on-premise systems, cloud databases, or APIs. The ideal data transformation tool should support files such as AVRO, Parquet, JSON, CSV, image data, and more. 

It is best to have a standard interim data format during transformations. Standardization efficiently unifies all the disparate data sources and allows users to design transformations seamlessly regardless of the original data format.

Versioning, configurations and audit logs

In data transformation, it is essential to maintain a comprehensive record of changes to data configurations, schemas, and processing rules over time. This is achieved through automatic versioning, tracking all changes, and maintaining an audit log. These mechanisms ensure traceability and accountability for data quality and consistency.

  1. Automatic Versioning: Data transformation tools should ensure configurations, schemas, and transforms are automatically versioned, providing a clear snapshot of each alteration. This feature allows users to easily compare different versions and revert to previous states when necessary.
  2. Change Tracking: Monitoring and tracking changes to data and processing rules are crucial for maintaining data integrity. Data transformation platforms such as Nexla, keep a detailed record of all modifications, enabling users to identify and address any discrepancies or inconsistencies that may arise over time.
  3. Audit Logs: Audit log functionality provides a comprehensive record of all changes to data configurations, schemas, and processing rules. This feature enables traceability, allowing users to pinpoint the origin of data quality issues and determine the impact of specific changes on the overall data pipeline. In addition, audit logs establish accountability by recording which user made each change, fostering a culture of responsibility and collaboration within the organization.

By implementing these data transformation concepts, Businesses can ensure a robust and reliable data pipeline, delivering high-quality and consistent data to drive informed decision-making.

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Data lineage, schema drift, and data drift

Data lineage is a tracking mechanism for data transformations throughout the pipeline, providing insight into the source tables, join operations, and the logic applied during the process. It becomes exceptionally crucial when numerous intermediate tables are involved in a transformation, as it helps to clarify the reasons behind specific data outcomes.

Schema drift indicates the schema differences between the source, interim, and resulting tables. Data drift follows the same concept except for the data contained within the tables. Schema and data drift help you identify the causes of data inconsistencies like duplicate or missing data. 

Data monitoring for missing or redundant data

A good data transformation tool includes more than just data lineage, schema drift, and data drift capabilities. It should also highlight missing or redundant data automatically.

Additionally, it should include recommendations of what action a user can take to remediate the problem. It can suggest ways to de-duplicate the data or tweak the transformation logic.

Integration with the cloud and pre-built applications

Most modern data management platforms are housed either entirely or partially in the cloud. Therefore, a good data transformation tool should support cloud integration with major cloud providers such as Amazon Web Services (AWS), Microsoft Azure, and Google Cloud Platform (GCP). In particular, it should be compatible with cloud ETL services to be used in conjunction with data pipelines on the cloud.

Aside from cloud integration, the tool should also be able to pull data from APIs, databases, data warehouses, data streams, or file stores in a no-code manner. That is, without complex connector configurations.

Ease of use for non-technical users 

No-code and low-code platforms are democratizing traditionally specialized processes. With data transformation, it is no different. A data transformation tool should allow non-technical users to design transformation steps using UI elements. These tools enable businesses to save on costs by eliminating the need to hire highly technical personnel to build ETL pipelines.

No-code transformations rely on pre-built functions that users can drag and drop into their data workflows. These functions include data cleaning and normalization, aggregation and summarization, and data type conversions. Users do not need coding experience to use these functions effectively, making them ideal for business analysts and non-technical users.

On the other hand, low-code transformations require some coding knowledge but still use user-friendly interfaces to simplify the process. Users can write code in familiar languages such as SQL, Python, or JavaScript to perform more complex transformations. They can also leverage reusable user-defined functions to make the transformation process more efficient and streamlined.

By offering no-code and low-code transformation options, organizations can empower a broader range of users to work with data, from business analysts to data scientists. These approaches can reduce the time and effort required to process and manipulate data and allow users to focus on extracting valuable insights from their data.

Automated calculation for data characteristics

Statistical metrics like mean, mode, standard deviation, and others are frequently needed to derive insights from numerical data. The ideal data transformation tool should include automatically calculating these metrics because it makes it easy for users to understand their data. 

For instance, the standard deviation tells you how uniform your data is. A high standard deviation means that your data has a high degree of variability. A low standard deviation indicates that the values in your dataset are close to each other. These things can point users to the right transformation strategies because the right approach is often highly contextual.

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Common data transformation use cases

The sections below describe common use cases where a modern data transformation tool would be helpful:

Multiple data sources with different formats

Businesses commonly have multiple data sources across different media. For example, a retail business could have point–of–sale, website, and third-party data. Often, these data come in varying formats–structured, semi-structured, or unstructured. Businesses face the challenge of blending all this data to become usable by their business analysts. A data transformation tool can help by providing a no-code solution to create ETL pipelines that blend disparate data sources.

Massive amounts of “dirty” data

Businesses also have to deal with data that suffers from several inconsistencies, such as missing, duplicate, or mismatched data. Hence, data transformations must deal with said inconsistencies based on business logic.

Writing these transformations is error prone and difficult to test, especially when there are dozens or hundreds of tables. A good data transformation tool can do basic transformations in just a few clicks. More complex transformations can also be designed with low-code and click and configure mechanics.

Data that needs auditing

Many companies have years worth of unaudited data. Sometimes the company is not even sure what kind of data they have. There is a missed opportunity to leverage data to make better business decisions.

Additionally, laws in some countries now require companies like banking and healthcare companies to audit their data. Hence, data needs to be fed to ETL pipelines, where it will be tagged appropriately. Metadata should also be filled out to make it easier and faster for companies to find the correct data. Data transformation tools include built-in mechanisms for data auditing–which saves time over manually building the integration process into the pipeline.

Complex logic needed to make the data ready-to-use

Blending business data involves business logic typically handled by business analysts to be translated to data transformation steps. We see this in cases where data from disparate sources have to be standardized and joined to create views containing pre-computed data ready to use for insight extraction.

The process involves several steps, from joining tables on a set of keys to aggregating the resulting data with analytic functions or some unique business logic. Again, when built traditionally, these transformations are prone to error, hard to test, and time-consuming to review. Data engineers must work with business analysts to build transformations correctly, and any miscommunication can be disastrous.

A no-code/low-code transformation tool like Nexla can remove a significant deal of complexity by enabling the domain experts to build the transformations instead of having an engineering team work with analysts.

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Summary

An optimal transformation tool should be accessible to users with varying technical backgrounds, while supporting diverse file formats, versioning, configurations, audit logs, data drift, data monitoring, and cloud integration. This comprehensive approach empowers businesses to derive scalable and efficient data insights.

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