Entrada’s Post

The best #GenAI models will not succeed without good data. That’s why data engineers are even more critical today. This guide shares practical guidance on using AI tools for faster code dev, implementing data governance & more. Read here 👇

#DatabricksAssistant is your context-aware AI assistant that lets you query data through a conversational interface. Eliminate tedium, increase productivity + immersion & accelerate time to value. How can you get the most out of AI-assisted data engineering? Read these tips

𝐋𝐞𝐯𝐞𝐫𝐚𝐠𝐢𝐧𝐠 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐅𝐫𝐚𝐦𝐞𝐰𝐨𝐫𝐤𝐬 𝐰𝐢𝐭𝐡 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Predictive analytics has transformed how organizations anticipate and plan. However, what happens when datasets grow too large for traditional tools to handle?. This is where big data frameworks like Spark MLlib and H2O.ai solutions designed to integrate predictive analytics with distributed computing, enabling precise forecasting at scale come in. 𝐖𝐡𝐚𝐭 𝐢𝐬 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬? Predictive analytics is the process of using data, statistical models, machine learning, and artificial intelligence to forecast future trends and behaviors. By analyzing historical and current data, businesses can predict outcomes with remarkable accuracy. Techniques like regression analysis, decision trees, and neural networks power these predictions, allowing organizations to address important business questions. 𝐇𝐨𝐰 𝐁𝐢𝐠 𝐃𝐚𝐭𝐚 𝐄𝐧𝐡𝐚𝐧𝐜𝐞𝐬 𝐏𝐫𝐞𝐝𝐢𝐜𝐭𝐢𝐯𝐞 𝐀𝐧𝐚𝐥𝐲𝐭𝐢𝐜𝐬 Distributed Processing: Handle massive datasets across multiple nodes for faster, more efficient computation. Enhanced Model Accuracy: Leverage ensemble learning methods and neural networks to identify patterns in complex data. Real-Time Insights: Predict trends as they happen, empowering businesses to respond quickly. 𝐑𝐞𝐚𝐥-𝐖𝐨𝐫𝐥𝐝 𝐀𝐩𝐩𝐥𝐢𝐜𝐚𝐭𝐢𝐨𝐧𝐬 🕵Fraud Detection: Banks analyze transactional networks in real time to identify anomalies that indicate fraud. ⚙️Operational Efficiency: Retailers forecast demand to optimize inventory and reduce waste. 👨👨👧👧Customer Segmentation: Marketers predict customer behaviors, enabling personalized campaigns for better engagement. 🔧Predictive Maintenance: Manufacturers predict equipment failures, preventing costly downtime. Combining predictive analytics with big data frameworks empowers businesses to tackle larger, more complex datasets without compromising accuracy or speed. #EvolutionOne #Analytics #PredictiveAnalytics #BigData

Strong data unlocks powerful AI. As a Databricks partner, we build that foundation for success. We deliver data engineering expertise for: ✔️ Complex AI & real-time analysis on Databricks ✔️ Clean, machine-learning-ready data ✔️ Secure & compliant data practices Learn more about how we turn data into a strategic asset: https://hubs.ly/Q02zcl_J0 #databricks #ai #fortegroup #dataengineeringing

#Topics The data practitioner for the AI era [ad_1] Data practitioners are among those whose roles are experiencing the most significant change, as organizations expand their responsibilities. Rather than working in a siloed data team, data engineers are now developing platforms and tools whose design improves data visibility and transparency for employees across the organization, including analytics engineers, data scientists, data analysts, machine learning engineers, and business stakeholders. This report explores, through a series of interviews with expert data practitioners, key shifts in data engineering, the evolving skill set required of data practitioners, options for data infrastructure and tooling to support AI, and data challenges and opportunities emerging in parallel with generative AI. The report’s key findings include the following: The foundational importance of data is creating new demands on data practitioners. As the rise of AI demonstrates the business importance of data more clearly than ever, data practitioners are encountering new data challenges, increasing data complexity, evolving team structures, and emerging tools and technologies—as well as establishing newfound organizational importance. Data practitioners are getting closer to the busi...

Data's not just big anymore, it's powerful! But how do we unlock its potential? Data Engineers are the secret weapon to transform raw data into actionable insights They build the pipelines that make this magic happen ️ 2024's Big Data Trends: Real-time Decisions: Make faster choices with data analyzed as it's generated AI & Machine Learning Power: Unlock hidden patterns & personalize experiences Unstructured Data Goldmine: Text, social media, sensor data - a treasure trove of insights Democratized Data: Empower everyone for data-driven innovation The Key? Reliable Data Pipelines Clean, secure data is the foundation for trustworthy analytics. Data Engineers build the bridge between raw data and powerful insights. Ready to unlock the power of Big Data? #dataengineering #bigdata #datascience

AI is impossible without data engineering. In fact, data engineering is the foundation of all things data. You just cannot go without it. The data has to come from somewhere. And it must be available. It must also be consistent and clean. And that can't be achieved with AI. It can only be done with the engineering steps. And those step are the ones of data engineering. To take the data from where it is. And send it to where it needs to be. So that when someone needs to use it, they can. #data #analytics #ai

In the era of advanced pre-trained models, the key differentiator is no longer just building models from scratch but providing high-quality data for model tuning. The critical first step? Exploratory Data Analysis (EDA). Here are 10 crucial aspects to focus on in EDA: 1. Source and Refresh Cycle: Identify where your data comes from, its update frequency, and refresh cycle. 2. Data Types: Understand the different data types present in your dataset. 3. Duplicate Values: Check for duplicates in your dataset. 4. Missing Information: Look for any missing data points. 5. Balance of Data: Evaluate if your data is skewed or balanced. 6. Statistical Overview: Review essential statistics like mean, median, and record count. 7. Data Distribution and Outliers: Analyze your data’s distribution and pinpoint outliers. 8. Normalization Needs: Determine if normalization is necessary for effective analysis. 9. Correlations: Investigate possible correlations between features. 10. Trends and Seasonality: Explore any evident trends or seasonal patterns. Understanding the key aspects to explore during EDA is crucial for effective feature engineering, which in turn ensures high-quality data input for model tuning! #ai #ml #datascience

#Topics The data practitioner for the AI era [ad_1] Data practitioners are among those whose roles are experiencing the most significant change, as organizations expand their responsibilities. Rather than working in a siloed data team, data engineers are now developing platforms and tools whose design improves data visibility and transparency for employees across the organization, including analytics engineers, data scientists, data analysts, machine learning engineers, and business stakeholders. This report explores, through a series of interviews with expert data practitioners, key shifts in data engineering, the evolving skill set required of data practitioners, options for data infrastructure and tooling to support AI, and data challenges and opportunities emerging in parallel with generative AI. The report’s key findings include the following: The foundational importance of data is creating new demands on data practitioners. As the rise of AI demonstrates the business importance of data more clearly than ever, data practitioners are encountering new data challenges, increasing data complexity, evolving team structures, and emerging tools and technologies—as well as establishing newfound organizational importance. Data practitioners are getting closer to the busi...

#Topics The data practitioner for the AI era [ad_1] Data practitioners are among those whose roles are experiencing the most significant change, as organizations expand their responsibilities. Rather than working in a siloed data team, data engineers are now developing platforms and tools whose design improves data visibility and transparency for employees across the organization, including analytics engineers, data scientists, data analysts, machine learning engineers, and business stakeholders. This report explores, through a series of interviews with expert data practitioners, key shifts in data engineering, the evolving skill set required of data practitioners, options for data infrastructure and tooling to support AI, and data challenges and opportunities emerging in parallel with generative AI. The report’s key findings include the following: The foundational importance of data is creating new demands on data practitioners. As the rise of AI demonstrates the business importance of data more clearly than ever, data practitioners are encountering new data challenges, increasing data complexity, evolving team structures, and emerging tools and technologies—as well as establishing newfound organizational importance. Data practitioners are getting closer to the busi...