Information that does not conform to a predefined data model or schema.
Comprises 80-90% of all new data generated, offering immense value if harnessed effectively.
Its complexity and lack of structure, however, challenge traditional data infrastructure stacks.
There’s sometimes a misconception that investing in unstructured data infrastructure is unnecessary because AI models can learn directly from raw data.
Models trained on noisy or irrelevant data produce unreliable results
Preprocessing steps like data cleansing and normalization are essential in improving model accuracy and reducing computational costs.
Preprocessing reduces dimensionality and complexity, leading to faster training and lower resource consumption
As organizations increasingly recognize its potential, a new unstructured data stack has emerged, consisting of three core components: data extraction and ingestion, data processing, and data management.
1. Data Extraction and Ingestion
This step captures, extracts, transforms, and optimizes unstructured data for storage and further use.
Strawman Argument: “Traditional ETL processes are sufficient for handling unstructured data”
Rebuttal: This perspective underestimates the complexities involved in extracting meaningful information from unstructured sources
A. Capture and Extract:
Sources include social media, customer feedback, emails, and beyond.
Techniques: web scraping, API integrations, file parsing.
Teams may create custom extractors or rely on pre-built solutions to achieve high extraction accuracy.
Tech:
Web Scraping and APIs:
Tools like Scrapy and BeautifulSoup facilitate web scraping
Headless browsers like Puppeteer can handle dynamic content
File Parsing:
Handling diverse file formats (PDFs, DOCX, images) requires specialized parsers
Libraries like Apache Tika provide content detection and extraction
Advanced Extraction Tools:
Unstructured.io: Uses machine learning to parse complex documents
Lume AI: Specializes in natural language understanding to extract insights from textual data
Computer Vision in Data Extraction:
New startups use advanced computer vision to extract data from visual content
Unlike older Intelligent Document Processing (IDP) services using OCR, these modern tools leverage vision models to improve parsing accuracy, particularly for text-dominant modalities used by large language models (LLMs).
Partition and Optimize:
Data is semantically partitioned into smaller, logical units for contextual relevance.
Eg. Semantic Segmentation: Topic modeling and clustering algorithms partition data into coherent units
Results are formatted in machine-readable structures (e.g., JSON), enabling preprocessing tasks like cleaning and embedding generation.
Storage Destination:
Extracted data is stored in scalable systems like object storage data lakes or databases, ready for use in applications such as Retrieval-Augmented Generation (RAG).
Object Storage Systems: Solutions like Amazon S3 or Apache Hadoop’s HDFS provide scalable storage
Databases Optimized for Unstructured Data: NoSQL databases like MongoDB or Elasticsearch offer flexible schemas and powerful querying
Key Considerations:
Extraction Accuracy: Incorporating feedback loops and human-in-the-loop mechanisms can enhance accuracy
Performance: Parallel processing and hardware acceleration can address performance bottlenecks
Multimodal Support: Handling different data types in a unified pipeline is increasingly important
2. Data Processing
Unstructured data undergoes further transformation and analysis to unlock its full utility.
Strawman Argument: “Once the data is extracted, processing unstructured data is no different from processing structured data”
Rebuttal: This overlooks the unique challenges posed by unstructured data during processing
Transformation and Cleansing:
Cleansing ensures data consistency, while normalization prepares it for downstream applications.
Data Cleansing: Spell correction, stop-word removal, tokenization for text data
Normalization: Converting data into a consistent format
Feature Engineering: Word embeddings and contextual embeddings transform textual data for machine learning
Processing Engines:
Categorized by their focus (structured vs. unstructured data), scalability (single-node vs. distributed), and languages (SQL vs. Python)
Horizontal Scaling: Distributing workloads across multiple nodes
Hardware Acceleration: Utilizing GPUs, TPUs, or FPGAs to accelerate computationally intensive tasks
Real-Time Processing: Stream processing systems like Apache Flink or Kafka Streams handle continuous data flows
Distributed Computing: Leveraging frameworks for parallel processing
Popular engines like Spark, Dask, and Modin cater primarily to structured data, but emerging tools like Daft are gaining attention for their ability to handle multimodal data efficiently in distributed environments.
Scalability Challenges:
Memory Management: Data streaming and on-the-fly processing can mitigate memory constraints
Compute Optimization: Hardware accelerators and optimized algorithms can address compute-intensive tasks
3. Data Management
Strawman Argument: “Data management principles are universal; the same strategies used for structured data apply to unstructured data”
Rebuttal: Unstructured data introduces complexities in storage optimization, metadata management, and governance
The backbone of the unstructured data stack, data management encompasses the organization, storage, and governance of unstructured data.
Key Functions:
Organizing and storing data to ensure easy retrieval and analysis.
Metadata Management: Robust metadata schemas using JSON Schema etc
Indexing: Inverted indices for rapid retrieval of unstructured text data
Implementing data governance policies for compliance, security, and privacy.
Access Control: Role-based and attribute-based access controls
Audit Trails: Logging data access and modifications for compliance and forensics
Regulatory and Privacy Safeguards:
Policies control data access and usage, safeguarding sensitive information while empowering data-driven decision-making.
File Formats and Challenges:
Apache Parquet, a widely adopted column-oriented format, is prevalent in object storage systems but has limitations:
Full-page loading for single-row lookups is inefficient for random, single-row lookups common in unstructured data
Handling wide columns typical of unstructured data is resource-intensive.
Limited encoding options and metadata constraints at the page level hinder performance.
Conclusion
The unstructured data stack is still in its infancy. It needs to work and will eventually work as companies can transform this untapped resource into a competitive advantage. The stack’s evolution will undoubtedly shape the future of data infrastructure.