Big Data Analytics Tools

AI is only as powerful as the data it learns from. But raw data alone isn’t enough—it needs to be collected, processed, structured, and analyzed before it can drive meaningful AI applications.  How does data transform into AI-driven insights? Here’s the data journey that powers modern AI and analytics:  1. 𝗚𝗲𝗻𝗲𝗿𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – AI models need diverse inputs: structured data (databases, spreadsheets) and unstructured data (text, images, audio, IoT streams). The challenge is managing high-volume, high-velocity data efficiently.  2. 𝗦𝘁𝗼𝗿𝗲 𝗗𝗮𝘁𝗮 – AI thrives on accessibility. Whether on AWS, Azure, PostgreSQL, MySQL, or Amazon S3, scalable storage ensures real-time access to training and inference data.  3. 𝗘𝗧𝗟 (𝗘𝘅𝘁𝗿𝗮𝗰𝘁, 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺, 𝗟𝗼𝗮𝗱) – Dirty data leads to bad AI decisions. Data engineers build ETL pipelines that clean, integrate, and optimize datasets before feeding them into AI and machine learning models.  4. 𝗔𝗴𝗴𝗿𝗲𝗴𝗮𝘁𝗲 𝗗𝗮𝘁𝗮 – Data lakes and warehouses such as Snowflake, BigQuery, and Redshift prepare and stage data, making it easier for AI to recognize patterns and generate predictions.  5. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 – AI doesn’t work in silos. Well-structured dimension tables, fact tables, and Elasticube models help establish relationships between data points, enhancing model accuracy.  6. 𝗔𝗜-𝗣𝗼𝘄𝗲𝗿𝗲𝗱 𝗜𝗻𝘀𝗶𝗴𝗵𝘁𝘀 – The final step is turning data into intelligent, real-time business decisions with BI dashboards, NLP, machine learning, and augmented analytics.  AI without the right data strategy is like a high-performance engine without fuel. A well-structured data pipeline enhances model performance, ensures accuracy, and drives automation at scale.  How are you optimizing your data pipeline for AI? What challenges do you face when integrating AI into your business? Let’s discuss.  

She started invoicing her company for data requests. $200 per PowerPoint. $500 per dashboard. What happened next: It began as a joke during her performance review. "You say I'm not strategic enough," she told her manager. "But I spend 60% of my time on executive data requests." "That's part of the job," he replied. That night, she built a simple system. Every data request generated an internal invoice: - Time required - Hourly rate - Opportunity cost - Total "charge" She didn't send them. Just tracked them. Month 1 total: $18,400 Month 2 total: $22,100 Month 3 total: $19,750 During her next one-on-one, she presented the receipts. "I've generated $60,250 in data services this quarter. My actual job contributed $0. Which one should I prioritize?" Her manager went pale. She continued: "If we outsourced this to a data analyst at $50/hour, it would cost the company 75% less. And I could do my actual job." Word spread. Other employees started tracking their "invoices." The numbers were staggering: Engineering: $147,000/month in data services Product: $89,000/month in reporting Design: $34,000/month in presentations Someone built a company-wide dashboard: "Internal Data Services Inc." Running total: $4.2M annually The CFO called an emergency meeting. "This is ridiculous. You don't actually invoice internally." Someone responded: "Why not? Every external agency does. We're just the agency that also tries to do our real jobs." That's when it clicked. They were running two companies: 1. The actual business 2. An internal data agency with no billing department The CFO did what CFOs do. Ran an ROI analysis. Option A: Keep status quo ($4.2M hidden cost) Option B: Hire 3 dedicated analysts ($350K) Option C: Buy proper tools and train execs ($100K) The decision took five minutes. Within 30 days: - Executives learned self-service dashboards - Three analysts hired for complex requests - "Invoice system" retired The woman who started it all? Got promoted to Chief of Staff. First initiative: "Time is Money" visibility program. Now every team tracks the true cost of interruptions. Not to invoice. To inform. Because when you make invisible costs visible, behavior changes instantly. The company motto became: "Would you pay $500 for that PowerPoint? Then don't ask someone else to." Revenue grew 40% the next year. Not from new features. From people actually building them. Try it at your company. Track the invoice you'll never send. Watch how fast things change. Because nothing shifts behavior like a price tag.

I’ve worked in data science for a decade, and I’ve seen the field evolve a lot. But nothing compares to what’s happened in the last three. Generative AI has completely reshaped our workflows. What used to take weeks of manual data prep and iteration now happens in days or even hours. The role of a data scientist is shifting fast: less about repetitive coding, more about designing intelligent workflows that solve real business problems. I recently came across Google's new Practical Guide to Data Science, and here are a few insights that stood out for me: ➝ The agentic shift Most of a data scientist’s day used to be cleaning data, tuning models, and writing the same pipelines again and again. Now AI agents automate those parts. The value we bring is moving to analysis, interpretation, and driving business outcomes. ➝ Multimodal data For years, our work was limited to structured tables. But most enterprise data is unstructured like images, PDFs, audio, and free text. With BigQuery, you can now analyze this directly with SQL. That means questions that used to be impossible, like combining sales data with call transcripts, are finally within reach. ➝ Blending external intelligence with enterprise data Foundation models bring real-world knowledge into the enterprise stack. Instead of writing rules for every scenario, you can ask nuanced questions like: Which of our products show high satisfaction based on quality? This type of reasoning used to take months of manual analysis. ➝ AI as a feature engineering engine Instead of just running basic sentiment analysis, you can extract structured insights at scale. For example, pulling out sentiment specifically around “battery life” or “user interface” and joining it with sales data. Raw text turns into powerful features that drive models. ➝ In-place model development Moving data around used to be the bottleneck. With BigQuery ML, you can now train and deploy models right where the data lives. Teams have seen deployment times cut by 10x, shifting the focus from infrastructure to speed of insight. ➝ Vector embeddings and semantic search Vector search used to mean adding another system. Now it’s built into BigQuery. That means semantic product discovery, document retrieval, and multimodal analysis all within your data warehouse. Data scientists role is changing, and now it's less about syntax, more about strategy. Less about writing every line of code, more about designing AI-powered workflows. If you want to dive deeper, I recommend checking out the full guide. It’s packed with practical examples that show just how much the landscape has shifted

99% of Machine Learning courses teach you how to build ML models using static datasets. But real-world ML is a bit different... ↓ 𝗧𝗵𝗲 𝗽𝗿𝗼𝗯𝗹𝗲𝗺 🤔 An ML model can only generate business value once you plug it to a *live data sources*. There is no static CSV file, but constantly flowing data, that needs to be processed and fed into your ML model. And this is precisely what a *feature pipeline* does. 𝗧𝗵𝗲 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🧪 Is a program that 1️⃣ 𝗳𝗲𝘁𝗰𝗵𝗲𝘀 𝗱𝗮𝘁𝗮 from a data warehouse, Kafka topic or websocket, among others. 2️⃣ 𝘁𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝘀 this raw data into features 3️⃣ 𝘀𝗲𝗻𝗱𝘀 these feature data to storage (e.g. Feature Store) so the rest of the system can use it. Depending on the frequency at which the feature pipeline runs, we can distinguish between 2 types. - Batch feature pipeline - Streaming feature pipeline ➡️ 𝗕𝗮𝘁𝗰𝗵 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 🕒 A batch feature pipeline is a program, often written in Python or Spark that fetches data and generates features on a schedule, for example: - daily - hourly - every 10 minutes To implement a batch feature pipeline you need - 𝗰𝗼𝗺𝗽𝘂𝘁𝗶𝗻𝗴, for example, a GitHub VM or an AWS Lambda function. - 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻, to schedule and trigger the execution of the pipeline. Popular options are Apache Airflow and Prefect. ➡️ 𝗦𝘁𝗿𝗲𝗮𝗺𝗶𝗻𝗴 𝗳𝗲𝗮𝘁𝘂𝗿𝗲 𝗽𝗶𝗽𝗲𝗹𝗶𝗻𝗲 ⚡ A streaming pipeline is a program that is 𝗰𝗼𝗻𝘀𝘁𝗮𝗻𝘁𝗹𝘆 ingesting data (e.g. from → an external web socket, or → a message bus like Kafka processing it, and serving it downstream, either to → a message bus (e.g. Apache Kafka), or → a Feature Store. Stream-processing can be implemented either with → Apache Spark Streaming (JVM) → Apache Flink (JVM) → Bytewax (Python on top of Rust) → Pathway (Python on top of Rust) → Quix (pure Python) ------ Hi there! It's Pau 👋 Every day I share free, hands-on content, on production-grade ML, to help you build real-world ML products. 𝗙𝗼𝗹𝗹𝗼𝘄 𝗺𝗲 on LinkedIn and 𝗰𝗹𝗶𝗰𝗸 𝗼𝗻 𝘁𝗵𝗲 🔔 so you don't miss what's coming next #machinelearning #mlops #realworldml

Stories in People Analytics: The Future of SAP SuccessFactors Reporting Navigating reporting and analytics in SAP SuccessFactors can be overwhelming, especially with the diverse tools and capabilities across different modules. Here’s a quick snapshot of how reporting features vary across modules like Employee Central, Onboarding Compensation, and Performance & Goals. Here is the break down of reporting options by module. * Tables and Dashboards are the basics—great for quick overviews, but some modules have limitations. * Canvas Reporting is where you go for deeper, more detailed insights, especially for modules like Employee Central or Recruiting Management. * Stories in People Analytics is the standout—it’s available for every module and offers dynamic, unified reporting. * Some modules, like Onboarding 1.0, still rely on more limited options, reminding us that it’s time to upgrade where we can. Takeaway: Understanding which tools align with your reporting needs is critical for maximizing the value of SAP SuccessFactors. Whether you’re focused on operational efficiency or strategic insights, this matrix can serve as a guide to selecting the right tool for the right task. How are you approaching reporting in SuccessFactors? Are you fully on board with Stories yet? or are you still in the planning phase? Feel free to reach out if you’re looking for insights or guidance! #SAPSuccessFactors #HRReporting #PeopleAnalytics #HRTech #TalentManagement

Businesses leveraging AI-powered data analytics, including the latest advancements, are projected to see a 40% increase in operational efficiency. 🤯 In today's hyper-competitive landscape, the lag time between data generation and actionable insights can be the difference between thriving and just surviving. Traditional data analysis often involves manual, time-consuming processes, hindering agility and the ability to capitalize on emerging opportunities. The Autonomous Data & AI Revolution is Here! Google's Data & AI Cloud continues to evolve, and at #GoogleCloudNext #2025, they unveiled groundbreaking features that bring us closer to truly autonomous data operations. Imagine AI not just assisting, but proactively working with your data. 💡 𝐇𝐞𝐫𝐞 𝐚𝐫𝐞 3 𝐠𝐚𝐦𝐞-𝐜𝐡𝐚𝐧𝐠𝐢𝐧𝐠 𝐟𝐞𝐚𝐭𝐮𝐫𝐞𝐬 𝐚𝐧𝐧𝐨𝐮𝐧𝐜𝐞𝐝: 𝐀. 𝐒𝐩𝐞𝐜𝐢𝐚𝐥𝐢𝐳𝐞𝐝 𝐀𝐈 𝐀𝐠𝐞𝐧𝐭𝐬 𝐟𝐨𝐫 𝐄𝐯𝐞𝐫𝐲 𝐃𝐚𝐭𝐚 𝐑𝐨𝐥𝐞: Google is embedding intelligent agents directly into BigQuery and Looker, tailored to specific user needs. 1. 𝐃𝐚𝐭𝐚 𝐄𝐧𝐠𝐢𝐧𝐞𝐞𝐫𝐢𝐧𝐠 𝐀𝐠𝐞𝐧𝐭 (𝐆𝐀): Automates tedious tasks like data preparation, transformation, enrichment, anomaly detection, and metadata generation within BigQuery pipelines. This means data engineers can focus on building robust and trusted data foundations instead of manual cleaning. 2. 𝐃𝐚𝐭𝐚 𝐒𝐜𝐢𝐞𝐧𝐜𝐞 𝐀𝐠𝐞𝐧𝐭 (𝐆𝐀): Integrated within Colab notebooks, this agent streamlines the entire model development lifecycle, from automated feature engineering and intelligent model selection to scalable training. Data scientists can accelerate their experimentation and focus on advanced modeling. 3. 𝐋𝐨𝐨𝐤𝐞𝐫 𝐂𝐨𝐧𝐯𝐞𝐫𝐬𝐚𝐭𝐢𝐨𝐧𝐚𝐥 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 (Preview): Empowers all users to interact with data using natural language. Developed with DeepMind, it provides advanced analysis and transparent explanations, ensuring accuracy through Looker's semantic layer. A conversational analytics API is also in preview for embedding this capability into applications. 𝐁. 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲 𝐊𝐧𝐨𝐰𝐥𝐞𝐝𝐠𝐞 𝐄𝐧𝐠𝐢𝐧𝐞 (Preview): This leverages the power of Gemini to understand your data context deeply. It analyzes schema relationships, table descriptions, and query histories to generate metadata on the fly, model data relationships, and recommend business glossary terms. 𝐂. 𝐀𝐈-𝐏𝐨𝐰𝐞𝐫𝐞𝐝 𝐃𝐚𝐭𝐚 𝐈𝐧𝐬𝐢𝐠𝐡𝐭𝐬 𝐚𝐧𝐝 𝐒𝐞𝐦𝐚𝐧𝐭𝐢𝐜 𝐒𝐞𝐚𝐫𝐜𝐡 (𝐆𝐀) 𝐢𝐧 𝐁𝐢𝐠𝐐𝐮𝐞𝐫𝐲: Building on the Knowledge Engine, this feature allows users to uncover hidden insights and search for data using natural language. This makes data exploration more intuitive and accessible to a wider range of users. By embedding AI directly into the data lifecycle, organizations can achieve unprecedented levels of efficiency, agility, and insight generation. Follow Omkar Sawant for more! More details in the comments. #DataAnalytics #AI #GoogleCloudNext #Autonomous #Data #BigQuery #Looker #AI #LifeAtGoogle

Discover → Control → Trust → Scale Governance is not a tool. It’s a layered system: Catalog – discover, tag, and connect data + AI assets. Quality – enforce correctness, freshness, and reliability. Policy – codify who can do what, where, and how. AI Control – govern models, prompts, and usage. Break one layer → trust breaks. Good governance doesn’t slow data down — it makes it usable, trusted, and AI-ready. With so many tools out there, the real question is simple: what helps your team trust data faster? Here's the breakdown to adapt and integrate with Data Governance: ⚙️ 1. ENTERPRISE GOVERNANCE TOOLS Collibra – Enterprise‑grade governance platform for glossary, lineage, and policy‑driven stewardship. Atlan – AI‑powered data catalog that enables self‑service discovery and governance‑as‑code. Informatica Axon – Unified governance hub for policies, lineage, and MDM‑integrated data. Alation – AI‑driven catalog and search engine built for analyst‑centric discovery. OvalEdge – Governance and compliance platform focused on sensitive‑data detection and templates. Secoda – Lightweight AI catalog for modern data teams with simple issue tracking. ☁️ 2. CLOUD‑NATIVE GOVERNANCE Databricks Unity Catalog – Single governance layer for data and ML across the Databricks lakehouse. Google Cloud Dataplex – Unified data governance and profiling layer for GCP data lakes. Microsoft Purview – Cross‑Azure catalog, classification, and sensitivity‑label governance engine. Snowflake Horizon – Native governance and access control layer built into Snowflake. Google Cloud Data Catalog – Metadata discovery and integration layer for BigQuery and Vertex AI. 🔄 3. PIPELINE + QUALITY LAYER dbt Labs – Transformation‑forward framework that enforces data contracts and testing in pipelines. Great Expectations – Validation framework that codifies data quality expectations and tests. Soda – Observability tool for monitoring data freshness, distribution, and anomalies. ⚡How to decide, where to begin with? Single platform  → Start with Unity Catalog / Dataplex / Purview / Snowflake Horizon. Multi‑cloud  → Add Atlan / Collibra as cross‑platform governance. Data quality issues  → Enforce contracts with dbt + Great Expectations. The smartest governance stacks don’t rely on one tool, Instead they combine catalog, quality, lineage, and policy where each matters most. #data #engineering #AI #governance

Imagine you have 5 TB of data stored in Azure Data Lake Storage Gen2 — this data includes 500 million records and 100 columns, stored in a CSV format. Now, your business use case is simple: ✅ Fetch data for 1 specific city out of 100 cities ✅ Retrieve only 10 columns out of the 100 Assuming data is evenly distributed, that means: 📉 You only need 1% of the rows and 10% of the columns, 📦 Which is ~0.1% of the entire dataset, or roughly 5 GB. Now let’s run a query using Azure Synapse Analytics - Serverless SQL Pool. 🧨 Worst Case: If you're querying the raw CSV file without compression or partitioning, Synapse will scan the entire 5 TB. 💸 The cost is $5 per TB scanned, so you pay $25 for this query. That’s expensive for such a small slice of data! 🔧 Now, let’s optimize: ✅ Convert the data into Parquet format – a columnar storage file type 📉 This reduces your storage size to ~2 TB (or even less with Snappy compression) ✅ Partition the data by city, so that each city has its own folder Now when you run the query: You're only scanning 1 partition (1 city) → ~20 GB You only need 10 columns out of 100 → 10% of 20 GB = 2 GB 💰 Query cost? Just $0.01 💡 What did we apply? Column Pruning by using Parquet Row Pruning via Partitioning Compression to save storage and scan cost That’s 2500x cheaper than the original query! 👉 This is how knowing the internals of Azure’s big data services can drastically reduce cost and improve performance. #Azure #DataLake #AzureSynapse #BigData #DataEngineering #CloudOptimization #Parquet #Partitioning #CostSaving #ServerlessSQL

LinkedIn just made agency reporting 10x easier. The analytics dashboard got a complete makeover. And if you're managing client accounts or running an agency, this changes everything. Here's what's new: Along with daily impressions and followers, you can now see: • Compounded impressions over time • Cumulative engagement metrics • Follower growth trends in one view Why this matters for agency owners: Before, you had to piece together daily snapshots to show clients progress. Or worse, pay for third-party tools just to get basic trend data. Now? LinkedIn gives you the full picture natively. You can finally show clients: • How their reach compounds over weeks and months • Which content drives sustained engagement • Real growth patterns, not just daily spikes No more exporting CSV files. No more manual calculations. No more justifying another analytics tool subscription. The platform is doing the heavy lifting for you. This is huge for: Agency owners tracking multiple client accounts Marketers proving ROI to leadership Anyone who needs to show progress beyond vanity metrics LinkedIn is finally giving us the tools to measure what actually matters: momentum, not just moments. If you haven't checked out the new analytics yet, go look. It's a game-changer for how we report and optimize. What metrics do you track most closely for your clients or personal brand?

SQL on Streaming Data at Scale: Netflix Makes It Real #Netflix is pushing the boundaries of #StreamProcessing and #DataMesh by bringing SQL to the forefront of its data movement platform. Their latest innovation? An #ApacheFlink SQL Processor embedded into their #ApacheKafka-based architecture—democratizing stream processing across teams. Why does this matter? Traditional data pipelines often force engineers to build and maintain custom Flink jobs using low-level APIs. That’s powerful—but slow, hard to scale, and difficult for teams without deep stream processing experience. Netflix’s new SQL Processor flips the model: – Teams write declarative #SQL instead of Java code – Queries run interactively against live #Kafka topics – Schema inference, real-time validation, and autoscaling come built-in – Developers iterate in seconds, not sprints This reduces latency, resource overhead, and the need for siloed “streaming experts.” It also enables rapid adoption of streaming transformations across use cases—while preserving guardrails for performance and reliability. The result? A scalable, developer-friendly foundation for stream-first pipelines, enriched with tools like Flink’s Table API, #ApacheIceberg, and Kafka’s decoupled design. Netflix’s approach shows what’s possible when real-time meets usability: https://lnkd.in/eDqUmbR4 Could SQL-first stream processing help your teams build faster, more reusable data products?