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.  

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

The "Data Warehouse" isn't a storage box anymore — it’s a "System of Action" BigQuery just got smarter with the new updates! Not with noise — but with direction. We’ve moved from "Big Data" to the Agentic Data Cloud. Here is why your 2026 architecture just became legacy: 𝗣𝗲𝗿𝗳𝗼𝗿𝗺𝗮𝗻𝗰𝗲 𝗶𝘀 𝗻𝗼𝘄 𝗶𝗻𝘃𝗶𝘀𝗶𝗯𝗹𝗲 With Fluid Scaling (GA) and Adaptive Execution, traded manual slot tuning for per-second billing. → 34% lower costs on average. → Less time firefighting, more time building. 𝗢𝗽𝗲𝗻 𝗯𝘆 𝗱𝗲𝗳𝗮𝘂𝗹𝘁 (𝗧𝗵𝗲 𝗜𝗰𝗲𝗯𝗲𝗿𝗴 𝗘𝗿𝗮) BigQuery is now a Borderless Lakehouse. Bi-directional federation with Snowflake and Databricks is standard. → No more data lock-in. → One engine, every format (Iceberg, Delta, Hudi). 𝗔𝗜 𝗶𝘀 𝘁𝗵𝗲 𝗢𝗦, 𝗻𝗼𝘁 𝗮 𝗹𝗮𝘆𝗲𝗿 With AI Optimized Mode, task-specific models are trained on the fly inside the engine. → 230x reduction in token consumption. → Functions like AI.PARSE_DOCUMENT are natively built-in. 𝗔𝗻𝗮𝗹𝘆𝘁𝗶𝗰𝘀 𝗺𝗲𝗲𝘁𝘀 𝗚𝗿𝗮𝗽𝗵 BigQuery Graph (Preview) lets you model complex relationships with GQL. → Power multi-hop reasoning for AI agents. → No extra graph DB stack required. 𝗚𝗼𝘃𝗲𝗿𝗻𝗮𝗻𝗰𝗲 𝗮𝘀 𝘁𝗵𝗲 𝗚𝗿𝗼𝘂𝗻𝗱𝗶𝗻𝗴 𝗟𝗮𝘆𝗲𝗿 The Knowledge Catalog (RIP Dataplex) is now the semantic brain. → It autonomously tags data to "ground" AI agents in truth. → Data Engineers are now the architects of Intelligence. Explore the latest updates on "What’s new in BigQuery: Powering the Agentic Era" - https://lnkd.in/dQB9e-5E If your strategy is still "Warehouse + ETL," you're behind. BigQuery is now: Data + AI + Platform in a single layer. #BigQuery #data #engineering

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

In an era where data sharing is essential and concerning, six fundamental techniques are emerging to protect privacy while enabling valuable insights. Fully Homomorphic Encryption involves encrypting data before being shared, allowing analysis without decoding the original information, thus safeguarding sensitive details. Differential Privacy adds noise variables to a dataset, making decoding the initial inputs impossible, maintaining privacy while allowing generalized analysis. Functional Encryption provides selected users a key to view specific parts of the encrypted text, offering relevant insights while withholding other details. Federated Analysis allows parties to share only the insights from their analysis, not the data itself, promoting collaboration without direct exposure. Zero-Knowledge Proofs enable users to prove their knowledge of a value without revealing it, supporting secure verification without unnecessary exposure. Secure Multi-Party Computation distributes data analysis across multiple parties, so no single entity can see the complete set of inputs, ensuring a collaborative yet compartmentalized approach. Together, these techniques pave the way for a more responsible and secure data management and analytics future. #privacy #dataprotection

🔐 Data in Use --Protection Strategies ⚠️ The Challenge When data is being processed in memory (RAM/CPU), it’s usually decrypted, which makes it vulnerable to: 💥 Insider threats 💥 Malware/memory scraping 💥 Cloud provider access ✅ Solutions for Data in Use 1. Homomorphic Encryption (HE) Data stays encrypted even during computation. Supports analytics, AI/ML, and calculations without exposing raw values. 💥 Use case: A hospital can run statistics on encrypted patient data without seeing individual records. Downside: Very slow for large-scale real-time workloads (still improving). 2. Secure Enclaves / Trusted Execution Environments (TEEs) Hardware-based isolation → a secure “enclave” inside the CPU where data is decrypted and processed. Even the system admin or cloud provider cannot see inside. ✨ Examples: 💥 Intel SGX 💥 AMD SEV 💥 AWS Nitro Enclaves → lets you isolate EC2 instances for secure key management, medical data processing, payment transactions, etc. 💥 Use case: A bank can run fraud detection models on sensitive financial data in the cloud without exposing it to AWS staff. 3. Confidential Computing Broader concept: combines TEEs, encrypted memory, and sometimes HE. Ensures that data remains protected throughout its lifecycle (rest, transit, use). ✨ Cloud examples: 💥 AWS Nitro Enclaves 💥 Azure Confidential Computing 💥 Google Confidential VMs 4. Secure Multi-Party Computation (MPC) Multiple parties compute a function jointly without revealing their private inputs. Often used in cryptocurrency custody, federated learning, and zero-knowledge proofs. 💥 Example: Banks collaboratively detect fraud patterns without sharing customer records. #learnwithswetha #encryption #datainuse #learning #dataprotection #privacy

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

Last year, I was speaking with a VP of Sales who confidently asserted: “Our buyers rely heavily on Gartner and Forrester reports, and LinkedIn is just noise.” That claim led us to a deeper look. So we ran a rapid social intelligence audit across their 10+ ideal enterprise target accounts and the reality was revealing: 👉 significant stakeholders actively adding connections in LinkedIn. 👉 a few of those routinely engaged on LinkedIn content. This wasn’t casual scrolling… it was conscious participation and relationship building. Some buyers were raising ‘purchase-intent’ questions as well. All transparently surfaced on LinkedIn - in public threads and peer groups. Data illuminating exactly where the research action happens pre-RFP. We scripted a custom GTM strategy: 👍 Enterprise Signal Posts: Engineered deep-dive, persona-tagged case studies, optimized to get clipped into internal research decks and circulated among architects, PMOs, and senior engineers. 👍 Dark-Social Authority: By engaging in high-value vendor comparison (and likes) threads, our client’s leadership profiles gained credibility and trust inside private channels invisible to traditional analytics. 👍 Decision-Stage Content: Launched proof-backed narrative video for "solution-aware" prospects, resulting in high-conversion SQLs. With consistency. The outcomes? 💪 Significant % of new enterprise meetings originated directly from LinkedIn-driven content touchpoints and network engagement. 💪 RFP win-rate increased, correlated to significant buyers explicitly referencing LinkedIn case materials. 💪 Sales cycles compressed because buyers entered conversations highly informed and confident. Why does this work in enterprise buying cycles? Vendor Validation: B2B procurement is increasingly cross-functional; live peer discussions on LinkedIn serve as a real-time, trusted “research layer” far beyond static analyst reports. Peer Proof: Enterprise decision-makers weight peer-shared insights more heavily than vendor-curated collateral, especially within their own secure collaboration channels. If you’re still dismissing LinkedIn as “just noise,” you’re strategically ceding ground during arguably the most critical phase of buyer evaluation. In 2025, enterprise buying journeys don’t start with vendor meetings… they start with social proof, digital authority, and dark social signals. And the winners are the brands that embed themselves authentically and intelligently in these ecosystems. #SocialSelling #DarkSocial #LinkedIn #RevOps #AIGTM

As founder of a remote data company, I’m increasingly aware of the impact that remote working poses to data privacy. While the flexibility of remote work has been a welcome change for many, it also raises important questions about data security and privacy. Despite not having a centralised office, at Onyx Data we take a number of steps to ensure our clients' data is all handled securely. Here are some key points to consider:   Secure Access - It's essential to ensure that employees can access company resources securely from any location. Implementing strong VPNs and multi-factor authentication is a must.   Data Encryption - With sensitive information frequently shared across networks, we use end-to-end encryption for all data, both in transit and at rest.   Employee Training - Regular training on cybersecurity best practices can significantly reduce the risk of data breaches caused by human error.   Device Management - Utilising Mobile Device Management (MDM) solutions helps secure company data on personal devices used for work purposes. Remote work doesn’t have to come at the expense of protected data. It is possible to have both - successfully. I’d love to hear your thoughts in the comments below on on how we can better balance remote work and data privacy - what would you add to the list? #RemoteWork #DataPrivacy #Cybersecurity