Data Ingestion: Implementation Methods

As data velocity and volume grow, companies must adopt more sophisticated data ingestion methods to keep up with the demand. While some companies may have legacy data ingestion methods, it is crucial to consider the benefits of moving toward AI-powered data ingestion. AI-powered methods offer self-learning and automated processes that enable near-real-time, low-latency data ingestion, increasing how quickly decisions can be made.

This article will explore the different data ingestion implementation methods and highlight why companies should consider adopting the latest technologies to keep up with the evolving data landscape. It will also provide a step-by-step example of how AI-powered data ingestion works behind the scenes to help you gain a practical understanding of the steps being automated by a data engineering automation platform like Nexla.

Generations of data ingestion methods

Data ingestion has evolved significantly in recent years, driven by the increasing volumes and velocity of data. The table that follows lists out the generations of data ingestion methods along with the pros and cons of each.

Generation	Description	Pros	Cons	Risks with data integration
First	Manual scripts and custom coding for each source	Low upfront cost	Limited scalability and maintenance challenges	Data inconsistency, quality issues, and potential security risks
Second	Extract, transform, and load (ETL) tools	Increased automation and scalability	High upfront and ongoing costs as well as complexity	Potential data loss and quality issues and limited real-time capabilities
Third	Stream processing and microservices	Real-time ingestion and processing, flexibility, and scalability	High complexity and limited integration with existing systems	Potential data loss, quality issues, and operational risks
Fourth	AI-powered data ingestion	Self-learning, automation, and low latency	Limited adoption and relatively higher costs	Necessary to enforce rule-based policies that address potential data privacy and security concerns to ensure that data remains secure and regulation-compliant

Transitioning to advanced data ingestion methods

Earlier data ingestion generations, such as manual scripts, custom coding, and ETL tools, had several associated risks, including potential data loss, quality issues, and security concerns. In the case of manual scripts and custom coding, there is a risk of data inconsistency due to human error as well as maintenance challenges due to the complexity of custom code. With ETL tools, there is a high upfront cost, complexity, and limited real-time capabilities. Similarly, the third-generation stream processing and microservices have high complexity and limited integration with existing systems, increasing the potential for data loss and quality issues.

Moving toward data ingestion products that use AI-powered, self-learning, and automated methods with low latency, such as Nexla, is essential. AI-powered platforms that leverage machine learning algorithms to automatically discover, extract, and transform data from a wide range of sources provide several benefits, including the following:

• Low-latency ingestion and processing
• Automated mapping of data fields and relationships
• Continuous monitoring and alerting for potential issues
• Flexibility to integrate with existing systems and workflows

High-level data ingestion workflow

Additionally, mature data products such as Nexla comply with strict data security measures, such as SOC 2 Type 2 and GDPR. They also maintain certifications such as Privacy Shield, ensuring data privacy and security, thus mitigating security risks. 

Companies should strongly consider transitioning to cutting-edge data ingestion methods to stay competitive in the current data-driven business landscape while reducing the risks of data loss, security problems, and quality issues.

Key benefits of AI-powered data ingestion

AI-powered data ingestion products prioritize ease of use, flexibility, and scalability. By automating data ingestion, Nexla enables businesses to make real-time decisions based on data insights, providing improved operational efficiency, better customer experiences, and increased revenue.

One of the key benefits of implementing data products that leverage advanced data ingestion methods is their adherence to the best data security and privacy practices. 

Data products that use advanced data ingestion techniques powered by AI provide users with detailed auditing and logging capabilities, giving them complete visibility into data movement and access. 

The table below summarizes the features and benefits of AI-powered data ingestion platforms.

Feature/Benefit	Description
Self-learning	Designed to learn from data patterns and automate ingestion, reducing the need for manual intervention
Flexibility and scalability	Handles large volumes of data and can be easily customized to meet specific business needs
Real-time capabilities	Ingests and processes data in real time, allowing for immediate insights and actions
Data mapping and schema discovery	Automatically detects and maps data structures, reducing manual effort and increasing accuracy
Integration with existing systems	Seamlessly integrates with existing systems, allowing for streamlined data flow and improved efficiency
Data quality control	Ensures data accuracy and completeness, reducing the risk of errors and improving overall quality
Best practice adherence	Adheres to strict security and privacy procedures, including SOC 2 Type 2 certification and GDPR, HIPAA, and CCPA compliance
Detailed auditing and logging	Detailed logs of data movement and access, ensuring complete visibility and compliance.

Self-learning capabilities

Harnessing the power of AI can let you streamline data ingestion. AI-driven technology learns from data patterns, automating the process and reducing the need for manual intervention. This enables a more efficient and reliable ingestion process, saving time and resources.

Use Case: A large e-commerce company automated its data ingestion process by leveraging AI-powered technology, allowing it to analyze customer behavior patterns more efficiently. This resulted in improved product recommendations and increased sales. 

Flexibility and scalability

AI-powered data ingestion systems can adapt to various business requirements with ease. They can handle large data volumes while being customized to specific business needs. This ensures that the solution remains relevant and practical as your data landscape evolves.

Use Case: A rapidly growing startup utilized an AI-powered data ingestion system to scale its data pipeline as its customer base expanded, ensuring consistent data processing and insights across the organization.

Real-time capabilities

It’s possible to stay ahead of the curve with real-time data processing. AI-powered data ingestion allows for immediate data ingestion and processing, providing instant insights and facilitating faster, data-driven decision-making.

Use Case: A financial services firm implemented an AI-powered data ingestion solution, enabling real-time fraud detection and prevention by quickly processing and analyzing transaction data.

Best practice adherence

AI-powered data ingestion systems adhere to strict security and privacy procedures, including SOC 2 Type 2 certification and compliance with GDPR, HIPAA, and CCPA regulations. These systems ensure that your data remains secure and compliant with the latest regulations.

Use Case: A healthcare provider adopted an AI-powered data ingestion system to manage sensitive patient data, ensuring compliance with HIPAA regulations and maintaining strict security and privacy standards.

Detailed auditing and logging

AI-powered data ingestion systems provide detailed logs of data movement and access, ensuring transparency and facilitating compliance with regulatory requirements.

Use Case: An energy company used an AI-powered data ingestion solution to maintain detailed logs of data movement for regulatory compliance, ensuring accurate reporting and streamlined audits.

Best practices for data ingestion 

As companies continue to increase their reliance on data ingestion, it’s important for them to establish proper governance practices to ensure the accuracy and integrity of the data being ingested. Effective data ingestion governance helps maintain data quality, prevent data breaches, and ensure compliance with regulatory requirements. 
The following are some best practices for data ingestion governance.

Define clear data governance policies

Define clear data governance policies and procedures for data ingestion, outlining the roles and responsibilities of different stakeholders. Policies should also cover topics such as data quality, data retention, data security, and compliance with regulations like GDPR and HIPAA.

Automate data quality controls

Leverage automation using AI tools to establish data quality controls that ensure that ingested data is accurate and consistent. Employ data profiling, data cleansing, and data validation techniques, and utilize real-time monitoring of data ingestion to identify and resolve data quality issues in a timely manner.

Implement automated access controls

Integrate automated access controls to restrict access to sensitive data and prevent unauthorized access. Utilize role-based access control, two-factor authentication, and encryption techniques to enhance security. Automation can help manage and maintain access controls more effectively and consistently.

Maintain audit trails with automation

Automate the maintenance of audit trails to track data lineage and monitor access to sensitive data. Automated audit trails can help identify data breaches and provide evidence of compliance with regulatory requirements while ensuring comprehensive and accurate records.

Regularly review data governance policies with AI powered tools

Use AI-powered tools to regularly review and update data governance policies, ensuring that they remain relevant and practical. This includes conducting regular risk assessments, identifying new data sources, and staying current with regulation changes. Automation can continuously monitor policy compliance and identify areas for improvement.

Next-generation AI-powered data ingestion

Nexla’s next-generation data ingestion and automation platform leverages AI to automate the ingestion of data from a wide range of sources. It also provides data governance capabilities that enable users to manage data quality, access controls, and audit trails. The platform’s rule-based policies and automated alerts help ensure compliance with regulatory requirements.

For example, data quality control features enable users to monitor data accuracy and consistency in real time. The platform’s AI-powered algorithms can detect anomalies and flag potential data quality issues before they become significant problems.

Access control features enable users to restrict access to sensitive data and ensure that only authorized users can access the data. The platform’s audit trail features provide detailed logs of data movement and access, ensuring compliance with regulatory requirements like GDPR and HIPAA.

Simplify data integration with advanced data integration platforms  

Data integration is an important step in the overall process of data ingestion. Businesses find that integrating data from multiple sources can be complex due to varying data schemas and structures. Nexla simplifies this process with its AI-powered features and prebuilt connectors, making it easy for businesses to combine disparate data sources and maintain data quality.

 

Nexla data integration workflow (Source)

To better understand how Nexla simplifies the data integration process, let’s consider a retail company that wants to ingest data from point-of-sale systems, inventory systems, and customer feedback surveys. The data is in different formats and structures, making it challenging to map and transform data for analysis.

Below, we illustrate how Nexla’s AI-powered features facilitate data integration:

1. Automated data mapping and schema detection: Nexla’s platform automatically detects and maps data schemas from the three source systems, significantly reducing manual effort and increasing accuracy. This allows the retail company to easily combine the data from different sources Example of automated data mapping and schema detection for Oracle ADW (Source)
2. Data quality checks: Nexla ensures data accuracy and completeness by automatically applying data quality checks throughout the integration process. This reduces the risk of errors and improves the overall quality of the integrated data.
3. Anomaly detection: Nexla’s AI-powered anomaly detection feature identifies unusual data patterns or inconsistencies across the integrated data. This allows the retail company to quickly address potential data quality issues and maintain high-quality, reliable data.Example of output validation rule configurations (Source)
4. Prebuilt connectors: Nexla offers pre-built connectors powered by AI, eliminating the need for writing code to integrate data from popular applications. This simplifies the data integration process, making it faster and more efficient.A data analytics system with data connectors to disparate data sources (Source)

Using Nexla’s AI-powered features, the retail company can easily integrate data from multiple sources into a unified data source for analysis. 

Hardcoded data integration, step by step

Let’s demystify how this type of automation works behind the scenes by stepping through a hard-coded example. The scenario we will use is integrating data for a retail company from three sources—a point-of-sale system, an inventory system, and customer feedback surveys—using the spaCy Python package. We will cover the step-by-step process of mapping data fields to a standard schema using natural language processing (NLP) models and transforming the data to a tabular view. 

By the end of this section, you should better understand how AI-powered data integration can simplify the process of combining multiple data sets into a unified data source for analysis. You’ll also gain an appreciation for Nexla’s prebuilt connectors powered by AI that bypass the need for writing code to integrate data from popular applications.

Below you will find an example of the different data schemas and structures for each of the source systems. 

Point-of-sale system data:

Transaction_ID	Product_Name	Product_Price	Customer_ID	Transaction_Date
1	T-Shirt	20	123	2022-01-01
2	Jeans	50	456	2022-01-02
3	Hoodie	30	789	2022-01-02

Inventory system data:

Product_Code	Product_Name	Product_Description	Quantity
001	T-Shirt	Blue T-Shirt	100
002	Jeans	Black Jeans	50
003	Hoodie	Grey Hoodie	75

Customer survey system data:

Survey_ID	Customer_ID	Product_Name	Rating	Feedback
1	123	T-Shirt	4	Great quality and comfortable fit!
2	456	Jeans	3	The sizing is a bit off, but overall okay.
3	789	Hoodie	5	Love this hoodie, perfect for chilly weather!

To integrate the data sources, we first need to map the data fields to a common schema. We can use AI-powered Python libraries to map the data fields automatically:

1. Install the spaCy library using the following command:

   !pip install spacy

2. Load the spaCy English language model using the following code:

   import spacy
   
   nlp = spacy.load('en_core_web_sm')

3. Define the mapping function as follows:

   def map_fields(doc, field_mapping):
       for token in doc:
           for field, mapping in field_mapping.items():
               if token.text in mapping:
                   doc._.set(field, token.text)

4. Define the field mapping as follows:

   field_mapping = {
       'Transaction_ID': ['transaction', 'id'],
       'Product_Code': ['product', 'code'],
       'Product_Name': ['product', 'name'],
       'Product_Price': ['product', 'price'],
       'Product_Description': ['product', 'description'],
       'Quantity': ['quantity'],
       'Customer_ID': ['customer', 'id'],
       'Transaction_Date': ['transaction', 'date'],
       'Survey_ID': ['survey', 'id'],
       'Rating': ['rating'],
       'Feedback': ['feedback']
   }

5. Apply the mapping function to each data source using the spaCy and pandas libraries:

   import pandas as pd
   import spacy
   
   # Load spaCy English language model
   nlp = spacy.load('en_core_web_sm')
   
   # Define mapping function
   def map_fields(doc, field_mapping):
       for token in doc:
           for field, mapping in field_mapping.items():
               if token.text in mapping:
                   doc._.set(field, token.text)
   
   # Define field mapping
   field_mapping = {
       'Transaction_ID': ['transaction', 'id'],
       'Product_Code': ['product', 'code'],
       'Product_Name': ['product', 'name'],
       'Product_Price': ['product', 'price'],
       'Product_Description': ['product', 'description'],
       'Quantity': ['quantity'],
       'Customer_ID': ['customer', 'id'],
       'Transaction_Date': ['transaction', 'date'],
       'Survey_ID': ['survey', 'id'],
       'Rating': ['rating'],
       'Feedback': ['feedback']
   }
   
   # Import point-of-sale system data
   pos_data = pd.read_csv('point_of_sale_data.csv')
   
   # Map fields
   pos_docs = list(nlp.pipe(pos_data.to_dict('records')))
   for doc in pos_docs:
       map_fields(doc, field_mapping)
   
   # Convert to DataFrame
   pos_df = pd.DataFrame(pos_docs)
   
   # Import inventory system data
   inv_data = pd.read_csv('inventory_data.csv')
   
   # Map fields
   inv_docs = list(nlp.pipe(inv_data.to_dict('records')))
   for doc in inv_docs:
       map_fields(doc, field_mapping)
   
   # Convert to DataFrame
   inv_df = pd.DataFrame(inv_docs)
   
   # Import customer feedback survey data
   survey_data = pd.read_csv('customer_survey_data.csv')
   
   # Map fields
   survey_docs = list(nlp.pipe(survey_data.to_dict('records')))
   for doc in survey_docs:
       map_fields(doc, field_mapping)
   
   # Convert to DataFrame
   survey_df = pd.DataFrame(survey_docs)

This code applies the mapping function to each data source using the spaCy library. The data sources include the point of sale system, inventory system, and customer feedback survey data. 

The mapping function maps the data fields to a common schema. The resulting DataFrames are pos_df, inv_df, and survey_df, which represent the mapped data from each source.

The output of the code provided above would be a unified DataFrame with the integrated data sources, mapped to a common schema using the spaCy library. The resulting DataFrame would have the following columns:

• Transaction_ID
• Product_Name
• Product_Price
• Customer_ID
• Transaction_Date
• Product_Code
• Product_Description
• Quantity
• Survey_ID
• Rating
• Feedback

Here is an example of what the output would look like in tabular format: 

Transaction_ID	Product_Name	Product_Price	Customer_ID	Transaction_Date	Product_Code	Product_Description	Quantity	Survey_ID	Rating	Feedback
1	T-Shirt	20	123	2022-01-01	001	Blue T-Shirt	100	1	4	Great quality and comfortable fit!
2	Jeans	50	456	2022-01-02	002	Black Jeans	50	2	3	The sizing is a bit off, but overall okay.
3	Hoodie	30	789	2022-01-02	003	Grey Hoodie	75	3	5	Love this hoodie, perfect for chilly weather!

Once you have the data in a database-friendly format, it is ready to be ingested to the desired target environment.

Summary of key concepts

Adopting AI-powered data ingestion methods is essential for companies to keep up with growing data volume and increasing speed. These advanced methods offer self-learning and automated processes, enabling low-latency ingestion, real-time insights, and improved decision-making.

Earlier generations of data ingestion had limited scalability and real-time capabilities while presenting risks such as data loss, quality issues, and security problems. AI-powered platforms provide numerous benefits, including automated mapping, continuous monitoring, and integration with existing systems and workflows, all while adhering to strict data security measures and providing detailed auditing and logging capabilities.

Industries such as e-commerce, financial services, and healthcare have already realized the advantages of AI-powered data ingestion, leading to improved operational efficiency, customer experiences, and revenue generation. Transitioning to advanced data ingestion methods is crucial for companies to stay competitive in the data-driven business landscape.