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What advanced ETL techniques can you use for unstructured data and non-relational sources?

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Extract, transform, and load (ETL) is a process of moving data from various sources to a target destination, such as a data warehouse or a data lake. ETL can help you integrate, cleanse, and standardize data for analysis and reporting. However, ETL can also be challenging when dealing with unstructured data and non-relational sources, such as text, images, audio, video, web pages, social media, or NoSQL databases. How can you overcome these challenges and leverage the value of unstructured and non-relational data? Here are some advanced ETL techniques that you can use.

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Ananya Ghosh Chowdhury

Data and AI Architect at Microsoft | Responsible AI Advocate | Public Speaker | Startup Advisor | Career Mentor |…
Ananya Nayak

Senior Data Engineer @ Onix | Ex Deloitte, Bosch | Microsoft Certified: Fabric Analytics Engineer Associate | Microsoft…
John 'Femi A.

Lead Solution Architect ◆ AI & Cloud Strategist ◆ Program & Project Management ◆ Data Scientist ◆ Digital…

1 Text mining and natural language processing

Text mining and natural language processing (NLP) are techniques that enable you to extract meaningful information from unstructured text data, such as customer reviews, emails, news articles, or transcripts. Text mining and NLP can help you perform tasks such as sentiment analysis, topic modeling, entity extraction, summarization, classification, and clustering. You can use text mining and NLP tools, such as Python libraries (e.g., NLTK, spaCy, Gensim) or cloud services (e.g., Google Cloud Natural Language, Amazon Comprehend, IBM Watson Natural Language Understanding), to preprocess, analyze, and transform text data into structured formats that can be loaded into your target destination.

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Ananya Nayak

Senior Data Engineer @ Onix | Ex Deloitte, Bosch | Microsoft Certified: Fabric Analytics Engineer Associate | Microsoft Certified: Azure Data Engineer Associate | Databricks Certified: Databricks Data Engineer Associate
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As per my recent experience with AI, I realized we can make use of Azure AI Speech on Microsoft AI to capture and process spoken data. Azure Cosmos DB is quite suitable for processing semi-structured data i.e. data which isn't present in a typical RDBMS system in the form of rows and columns. Azure AI services such as Azure AI Face and Azure AI Vision can be leveraged to detect and analyze faces and to process images of varying dimensions respectively.

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2 Image and video processing

Image and video processing are techniques that enable you to extract features, patterns, and insights from unstructured image and video data, such as logos, faces, objects, scenes, or actions. Image and video processing can help you perform tasks such as face recognition, object detection, optical character recognition, scene segmentation, and video summarization. You can use image and video processing tools, such as Python libraries (e.g., OpenCV, scikit-image, Pillow) or cloud services (e.g., Google Cloud Vision, Amazon Rekognition, IBM Watson Visual Recognition), to preprocess, analyze, and transform image and video data into structured formats that can be loaded into your target destination.

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Ananya Ghosh Chowdhury

Data and AI Architect at Microsoft | Responsible AI Advocate | Public Speaker | Startup Advisor | Career Mentor | Harvard Business Review Advisory Council Member | Marquis Who's Who Listee | Founder @AIBoardroom
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Also , azure has a suite of tools , one of them is azure document intelligence - it can extract text from images / tables within pdfs / handwritten forms

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3 Web scraping and crawling

Web scraping and crawling are techniques that enable you to collect and extract data from web pages, such as HTML, CSS, JavaScript, or JSON. Web scraping and crawling can help you gather data from various sources, such as e-commerce sites, news portals, social media platforms, or blogs. You can use web scraping and crawling tools, such as Python libraries (e.g., BeautifulSoup, Scrapy, Selenium) or cloud services (e.g., Apify, ParseHub, Octoparse), to preprocess, analyze, and transform web data into structured formats that can be loaded into your target destination.

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4 NoSQL and schemaless ETL

NoSQL and schemaless ETL are techniques that enable you to handle data from non-relational sources, such as MongoDB, Cassandra, Redis, or Elasticsearch. NoSQL and schemaless ETL can help you deal with data that is dynamic, heterogeneous, or complex, such as nested documents, arrays, or graphs. You can use NoSQL and schemaless ETL tools, such as Python libraries (e.g., PyMongo, Cassandra-driver, redis-py, elasticsearch-py) or cloud services (e.g., Google Cloud Dataflow, Amazon Glue, Azure Data Factory), to preprocess, analyze, and transform non-relational data into structured formats that can be loaded into your target destination.

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John 'Femi A.

Lead Solution Architect ◆ AI & Cloud Strategist ◆ Program & Project Management ◆ Data Scientist ◆ Digital Transformation Leader ◆ PhDc, MSc. ◆ International Development
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From my experience, for Smart Manufacturing: Utilize advanced ETL techniques for IoT and sensor data, enabling real-time decision-making and predictive maintenance. Implement cloud-based ETL solutions for scalability, seamlessly integrating ML parameters for enhanced predictive modeling. Fintech: Leverage robust ETL tools for diverse financial data, incorporating ML algorithms for fraud detection and personalized recommendations. Ensure real-time processing, utilizing ML-based parameters to refine financial modeling and improve decision-making. Fashion Prediction Products: Employ advanced ETL for unstructured data from social media, using NLP and ML-based image recognition for trend analysis.

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5 Data quality and validation

Data quality and validation are techniques that enable you to ensure the accuracy, completeness, consistency, and reliability of your data. Data quality and validation can help you detect and correct errors, outliers, duplicates, missing values, or anomalies in your data. You can use data quality and validation tools, such as Python libraries (e.g., pandas, PySpark, Dask) or cloud services (e.g., Google Cloud Data Quality, Amazon DataBrew, Azure Data Catalog), to preprocess, analyze, and transform your data into high-quality formats that can be loaded into your target destination.

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John 'Femi A.

Lead Solution Architect ◆ AI & Cloud Strategist ◆ Program & Project Management ◆ Data Scientist ◆ Digital Transformation Leader ◆ PhDc, MSc. ◆ International Development
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Mostly and over the years these have been useful: Continuous Monitoring: Establish continuous monitoring mechanisms to promptly identify emerging data quality issues. Real-time alerts and notifications provide immediate attention to critical anomalies, allowing for proactive intervention and maintenance of high-quality data. Iterative Improvement: Embrace an iterative approach for continuous improvement of data quality. Analyze user feedback, refine validation algorithms, and adapt to evolving data patterns, ensuring that data quality practices remain dynamic and responsive. Documentation and Metadata: Maintain comprehensive documentation and metadata as pillars of transparency in data management.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What advanced ETL techniques can you use for unstructured data and non-relational sources?

1

2

3

4

5

6

1 Text mining and natural language processing

2 Image and video processing

3 Web scraping and crawling

4 NoSQL and schemaless ETL

5 Data quality and validation

6 Here’s what else to consider

Data Management

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What advanced ETL techniques can you use for unstructured data and non-relational sources?

1

2

3

4

5

6

1 Text mining and natural language processing

2 Image and video processing

3 Web scraping and crawling

4 NoSQL and schemaless ETL

5 Data quality and validation

6 Here’s what else to consider

Data Management

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