Current enterprise operations survive on data, their foundation for strategic choices, business intelligence, and market superiority. Enterprise data continues to expand, and most of its information exists beyond structured databases in unstructured formats. Unstructured enterprise data includes text files, documents, emails, images, videos, and many other forms that contain valuable insights but remain hidden because of their intricate nature. Organisational integration of unstructured data presents a considerable yet rewarding business potential that companies can exploit effectively.
As a market leader, Snowflake offers a cloud-based data solution representing a significant industry breakthrough. Snowflake's cloud-based data platform gained fame from its structured and semi-structured data management expertise while adding full-scale unstructured data capability. The following post explains how Snowflake allows businesses to convert their unstructured text files and documents alongside other data into valuable insights while driving business results and growth.
Understanding Unstructured Data
Any information that lacks a predefined data model or schema definition qualifies as unstructured data. Unstructured data differs from structured data, which maintains its format through tables using rows and columns, since unstructured data remains primarily textual and unorganised. Common examples include :
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Business documents, PDFs, Word documents, and social media postings fall under text-based content.
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Multimedia: Images, audio recordings, and video files.
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The IoT sensor logs and medical scans represent specialized file types belonging to specific industries.
The 80-90% of enterprise-generated data falls under the category of unstructured data despite being more complicated to manage. The combination of extreme quantity, various file types, and disorganized structure creates significant problems for processing and analysis through traditional software tools. Numerous organizations encounter difficulties extracting valuable information from their disorganized data, resulting in untapped business potential. Snowflake provides the ideal solution for organizations dealing with these complex requirements.
Snowflake's Approach to Unstructured Data
The cloud-native design of Snowflake functions as a scalable platform that accepts and processes all different data types, including unstructured data forms. The data management system at Snowflake solves these challenges in the following way:
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Unstructured data can be stored in Snowflake through two flexible storage options: internal storage within Snowflake or external storage that works with AWS S3, Azure Blob Storage, and Google Cloud Storage. The system supports different enterprise storage, distribution, and operational workflow requirements.
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The directory tables offered by Snowflake function as metadata catalogs that collect information such as unstructured file names, their sizes, and timestamps for modification. The platform enables users to run SQL statements, simplifying their ability to manage files through their metadata.
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The external functions available in Snowflake connect users with processing tools from Amazon Textract and Google Cloud Document AI so they can extract text from documents and images. Snowflake uses these functions to process unstructured data better without needing external processing systems.
These features collectively make Snowflake a powerful platform for managing and unlocking value from unstructured data, bridging the gap between storage and actionable insights.
Transforming Enterprise Text Files and Documents
Text-heavy documents—such as contracts, customer feedback, and reports—are common unstructured data in enterprises. Snowflake transforms these files into usable insights through a combination of ingestion, processing, and analysis:
Fig 1: Enterprise Text Files and Documents -
Data Ingestion: Snowflake supports loading text files (e.g., CSV, JSON, or raw text) into its stages. Once ingested, these files can be processed using Snowflake’s tools or external integrations.
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Unstrctured Data: Using Snowpark, enterprises can leverage Python libraries to perform tasks like natural language processing (NLP) or keyword extraction. For example, a company might analyse customer emails to identify sentiment trends or extract critical clauses from legal documents.
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Upload to Snowflake: Snowflake allows automated file processing through its Streams and Tasks features, which work on incoming files as soon as they become available. These features maintain constant insights by facilitating automatic data retrieval from newly uploaded documents.
Suppose a business needs to review thousands of customer support emails. Snowflake can import emails, extract key phrases or sentiments using Snowpark, and transform unstructured text into structured data for reporting—all on one platform.
Integrating Unstructured Data with Structured Data
The magic happens when unstructured data combines with structured data, providing a 360-degree view of business operations. Snowflake accomplishes that integration very effectively:
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Unified Platform: Snowflake stores structured (i.e., sales transactions) and unstructured data (i.e., product reviews) within one setting, eliminating silos and simplifying access.
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Data Joining: One can join structured and unstructured data through SQL queries. For instance, a retailer can join sales volume with customer feedback to determine why specific products trail.
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Rich Analytics: This offers more robust analytics capabilities, such as connecting purchase activity with customer feedback to inform marketing efforts.
Breaking down data types of silos, Snowflake enables companies to uncover insights unavailable if they work with only structured or unstructured data.
Analytics and AI with Unstructured Data
Beyond basic processing, Snowflake supports advanced analytics and artificial intelligence (AI) to maximize the value of unstructured data:
Figure 2: Analytics and AI with Unstructured Data -
Machine Learning with Snowpark: Snowpark allows machine learning models to be created and deployed in Snowflake using libraries like TensorFlow or scikit-learn. For example, a healthcare provider could utilise medical images to contribute to diagnoses.
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Scalable Compute: Snowflake's architecture dynamically scales compute resources so that even larger-scale AI workloads—e.g., training models on video transcripts—run efficiently.
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Real-World Applications: A bank can use Snowflake to analyse news headlines for sentiment and predict market fluctuations, and a media company can analyse video metadata to suggest content to users.
These capabilities position Snowflake as an industry-leading platform for companies seeking to harness AI and analytics on their unstructured data.
Security and Governance of Unstructured Data
Unstructured data often contains sensitive data, so security and governance are essential. Snowflake addresses these issues with robust capabilities:
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Encryption: Data, including unstructured files, is encrypted at rest and in transit. Internal stages offer server-side encryption for additional safeguarding.
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Access Control: Role-based access control (RBAC) only limits users with valid permissions to view or process specific files while sensitive data remains secure.
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Compliance Tools: Audit logs and data masking enable companies to adhere to regulatory rules, such as GDPR or HIPAA, when handling unstructured data.
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Secure Sharing: Snowflake offers safe data sharing with external partners using scoped URLS while allowing access and utilisation control.
Case Studies and Success Stories
To highlight Snowflake’s impact, here are two hypothetical success stories:
Manufacturing Company: A manufacturing business uses Snowflake to process IoT sensor data (unstructured) and manufacturing data (structured). Based on such data, the company predicts equipment downtime, lowering downtime by 20%.
Media Firm: A streaming firm uses Snowflake to analyse video transcripts and viewer engagement metrics. It applies this to build targeted recommendations that boost subscriber retention by 1.
The above explains how Snowflake converts unstructured data into tangible business outcomes across industries.
Transform Your Unstructured Data with Snowflake
Unstructured data presents companies with opportunities and challenges. Snowflake, which can store, process, and analyse text files and documents, among other things, provides the whole solution to realising its potential. It empowers companies to convert raw data into strategic assets by combining unstructured and structured data, supporting future-proofed advanced analytics and AI functionality, and providing sophisticated security.
As the volume of unstructured data keeps growing, Snowflakes and similar companies will play increasingly vital roles in enabling businesses to gain an edge. Whether unlocking insights from customer surveys or powering AI-driven innovations, Snowflake is transforming how businesses leverage their unstructured data, unlocking the potential for a future built on data.
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