27 november 2024 Structured Data vs Unstructured Data Deel dit bericht The difference between structured and unstructured data is fundamental to data management and analytics. Here’s an overview of the two types: 1. Structured Data Definition: Structured data is well ordered and adheres to a specified structure or standard. It is kept in a way that allows for easy search and analysis. Examples include relational databases, spreadsheet tables, financial transactions, and customer records. Characteristics: Format: Tabular (rows and columns), with each column having a specific data type (e.g. integer, string, date). Storage: Typically stored in relational databases (e.g., SQL databases), which manage and retrieve data using structured query languages (SQL). Searchability: The preset structure makes it easy to search using queries. Analytics: Structured data is simpler to analyze with statistical and mathematical models, making it perfect for dashboards and reporting. 2. Unstructured Data Definition: Unstructured data has no specified format or organization, making it more difficult to handle and analyze. This sort of data does not fit easily into tables and requires more advanced ways of analysis. Examples include text documents, emails, social media postings, audio, video, photos, and sensor data. Characteristics: Format: Numerous unstructured data types and formats exist, which differ greatly in terms of content and storage methods. Storage: Databases built for non-tabular data, such as NoSQL databases (e.g., MongoDB) or data lakes. Searchability: Difficult to find and evaluate without expert tools. Analytics: Derive insights through increasingly advanced processing, such as machine learning techniques. Key Differences FeatureStructured DataUnstructured DataFormatTabular (rows and columns)Text, images, audio, video, etc.StorageRelational databases (SQL)NoSQL databases, data lakesSearchabilityEasily searchable and queryableRequires specialized toolsAnalyticsStraightforward, quantitativeRequires AI and machine learningExamplesSpreadsheets, CRM data, SQL tablesSocial media, videos, emails 3. Semi-structured data Semi-structured data falls somewhere in the center. This type does not fit nicely into structured databases, yet it retains certain organizing qualities. JSON and XML are popular formats used in online data and APIs, where data is kept in key-value pairs that provide some structure but not to the level of a relational database. Semi-structured data has greater flexibility than structured data and is simpler to deal with than unstructured data. Make Better Decisions with Generative AI-driven Answers from Unstructured Data. Using Qlik Answers. Contact us for a demo Mireille Erasmus Mireille Erasmus joined Bitmetric in June 2022 as a Marketing Manager after immigrating from South Africa. She loves working with people and firmly believes in the power of positivity. Mireille focuses on creating effective campaigns and building strong connections with customers. With a passion for using data to drive results, she combines creativity with a positive mindset to develop marketing strategies. When she’s not busy at work, you’ll find Mireille spending time with friends and family, walking her dog, or visiting local markets. Qlik Hoe kunnen we je ondersteunen? Barry beschikt over meer dan 20 jaar ervaring als architect, developer, trainer en auteur op het gebied van Data & Analytics. Hij is bereid om je te helpen met al je vragen. Bel ons Mail ons 17 april 2025 The 2025 Masters Summit for Qlik will be in Hamburg, Germany The Masters Summit for Qlik offers the next step in your journey towards becoming a Qlik specialist. In 3 days our team of experts, with over 50 combined years of Qlik experience in the field, will take your skills to the next level. Bitmetric Event Qlik Training 17 april 2025 Transformation flows for SQL and Microsoft SQL Server now supported in Qlik Cloud Government Qlik Cloud Government now supports transformation flows for SQL and Microsoft SQL Server. The SQL Expression processor enables writing SQL expressions to process data. Users can create graphical and custom SQL-based transformations in data pipeline projects, enhancing data reshaping capabilities. New Release Qlik 17 april 2025 AI processor for transformation flows now supported in Qlik Cloud Government Qlik Cloud Government now supports an AI processor in transformation flows, enabling easy use of Databricks and Snowflake AI capabilities. The processor offers seven AI functions for Databricks, including sentiment analysis, grammar correction, and translation, and four for Snowflake, such as sentiment analysis and text summary. This enhancement streamlines data processing and leverages advanced AI for improved insights. New Release Qlik
17 april 2025 The 2025 Masters Summit for Qlik will be in Hamburg, Germany The Masters Summit for Qlik offers the next step in your journey towards becoming a Qlik specialist. In 3 days our team of experts, with over 50 combined years of Qlik experience in the field, will take your skills to the next level. Bitmetric Event Qlik Training
17 april 2025 Transformation flows for SQL and Microsoft SQL Server now supported in Qlik Cloud Government Qlik Cloud Government now supports transformation flows for SQL and Microsoft SQL Server. The SQL Expression processor enables writing SQL expressions to process data. Users can create graphical and custom SQL-based transformations in data pipeline projects, enhancing data reshaping capabilities. New Release Qlik
17 april 2025 AI processor for transformation flows now supported in Qlik Cloud Government Qlik Cloud Government now supports an AI processor in transformation flows, enabling easy use of Databricks and Snowflake AI capabilities. The processor offers seven AI functions for Databricks, including sentiment analysis, grammar correction, and translation, and four for Snowflake, such as sentiment analysis and text summary. This enhancement streamlines data processing and leverages advanced AI for improved insights. New Release Qlik