Future-Proofing Data Platforms with Apache Spark Trends

Long Live Spark, Down with Spark 🙃 Spark has been the de facto processing engine for the last 10 years in the "Big Data" community. It still has its place in data processing. Some have branched off their own versions, spun off custom engines, and developed new approaches in an attempt to overcome the shortcomings of spark in diverse landscapes. Our focus is to enable the right compute for the problem with a focus on interoperability with other systems and to have open source compute engines as first-class citizens in platform. Enterprises are highly complex, with high heterogeneity of solutions, tech debt, data gravity, and modalities. I would argue that most pipelines in enterprise are not web scale; they are small and medium sized. A pipeline with a few million or hundreds of millions of rows, sometimes a few billion. Spark is overkill for 90%+ of these workloads and carries a lot of overhead that impacts speed and cost. With the Palantir Multi-Modal Data Plane (MMDP), we provide the framework to store data anywhere, compute data anywhere, and use any model. If we break down this a bit more, this means I can have data spread across different platforms, file formats, structures and use any compute (inside or outside of the platform) to process my data. The permutations of storage locations, formats, and compute engines are massive. It's the right tool for the problem at hand, not one tool you need to shove everything into. Re-platforming should never be the goal. With new frameworks like Polars, DataFusion, DuckDB and the availability of larger node sizes that this means you can run increasingly bigger and more complex workloads in a fraction of the time and cost. You can mix and match these as needed in Foundry and AIP, even having one step run one engine and another step use a different engine. (You can even bring your own engine.) Spark is always there on the table, but it should no longer be the default for most enterprises to start with.

"Most pipelines in enterprises are not web scale" Can't agree more with Chad Spark is very powerful but not for all your data engineering requirements. Life could be easier if engineers think before throwing Spark to non Spark problems.

Another Databricks product launch 🚨 alert: Our Data Science Agent 🧪🕶️(beta) is ready to go and rolled out to our customers. This is the first in a series of AI Data Agents coming to Databricks Assistant. It transforms the Assistant from a helpful copilot into a true autonomous partner. Fully integrated into Notebooks and the SQL Editor, the Data Science Agent can plan, execute, and refine entire workflows across the data science lifecycle — from exploratory analysis to feature engineering, model training, and evaluation.  So...short demo video bellow 📽️⬇️ and release blog is in the comments TL'DR: 🦾 𝘼𝙪𝙩𝙤𝙣𝙤𝙢𝙤𝙪𝙨 𝘿𝙖𝙩𝙖 𝙎𝙘𝙞𝙚𝙣𝙘𝙚 𝘼𝙜𝙚𝙣𝙩 Databricks Assistant has evolved from a copilot to an autonomous data science agent that can reason, plan, and execute complex, multi-step data workflows directly in Databricks.databricks 🧠 𝘾𝙤𝙣𝙩𝙚𝙭𝙩-𝘼𝙬𝙖𝙧𝙚 𝙄𝙣𝙩𝙚𝙡𝙡𝙞𝙜𝙚𝙣𝙘𝙚 The agent combines AI reasoning with Databricks’ Data Intelligence Platform and Unity Catalog to ensure answers are reliable, context-aware, and governed for trust and transparency 🎼 𝙋𝙡𝙖𝙣𝙣𝙚𝙧 𝙖𝙣𝙙 𝙒𝙤𝙧𝙠𝙛𝙡𝙤𝙬 𝙊𝙧𝙘𝙝𝙚𝙨𝙩𝙧𝙖𝙩𝙞𝙤𝙣 The new Planner feature allows the agent to propose step-by-step plans for complex tasks, getting user approval before execution and refining plans interactively, ensuring control and clarity in multi-stage processes 🚊 𝙐𝙨𝙚𝙧 𝘾𝙤𝙣𝙩𝙧𝙤𝙡 𝙖𝙣𝙙 𝙎𝙖𝙛𝙚𝙩𝙮 𝙂𝙪𝙖𝙧𝙙𝙧𝙖𝙞𝙡𝙨 Users retain full control: the agent asks for approval before running code, and built-in guardrails help prevent mistakes, delivering trustworthy automation while recommending code review for sensitive operations.

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 🚀 Data is no longer just a resource—it's a strategic asset. Organizations that learn to productize their data can unlock new revenue streams, drive operational efficiency, and deliver personalized user experiences at scale. At the core of this transformation are data products—modular, reusable outputs built from high-quality data that serve internal or external consumers. Think APIs, dashboards, curated datasets, ML-ready features, or embedded analytics. 📦 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 • Centralized repository (e.g., 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯, 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲) that can hold structured, semi-structured, and unstructured data at scale. 2️⃣ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 • Batch: Use Fivetran or Apache NiFi to sync data periodically • Real-Time: Stream with Apache Kafka or Amazon Kinesis 3️⃣ 𝗔𝗽𝗽𝗹𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Combine batch and stream processing for low-latency and comprehensive insights. ◦ Batch: Apache Spark ◦ Stream: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 4️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Use orchestrators like Apache Airflow or Dagster Labs to automate data flow, monitoring, and alerts. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗖𝘂𝗿𝗮𝘁𝗲 • Use tools like dbt Labs to standardize, model, and document data transformations into clean, trusted assets. 6️⃣ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 • API endpoints, visualization dashboards (Power BI, Tableau), or shared datasets for clients and internal teams. 🔍 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗲𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Use data products to: • Personalize customer journeys in real time • Optimize supply chains using predictive analytics • Launch new services based on market behavior insights • Drive efficiency through internal self-serve data tools 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Data collection for these products often requires accessing region-specific or protected online sources. Residential proxy infrastructure like https://lnkd.in/gtBic3Jd ensures reliable, high-speed data sourcing while maintaining compliance and performance.

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 🚀 Data is no longer just a resource—it's a strategic asset. Organizations that learn to productize their data can unlock new revenue streams, drive operational efficiency, and deliver personalized user experiences at scale. At the core of this transformation are data products—modular, reusable outputs built from high-quality data that serve internal or external consumers. Think APIs, dashboards, curated datasets, ML-ready features, or embedded analytics. 📦 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 • Centralized repository (e.g., 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯, 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲) that can hold structured, semi-structured, and unstructured data at scale. 2️⃣ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 • Batch: Use Fivetran or Apache NiFi to sync data periodically • Real-Time: Stream with Apache Kafka or Amazon Kinesis 3️⃣ 𝗔𝗽𝗽𝗹𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Combine batch and stream processing for low-latency and comprehensive insights. ◦ Batch: Apache Spark ◦ Stream: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 4️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Use orchestrators like Apache Airflow or Dagster Labs to automate data flow, monitoring, and alerts. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗖𝘂𝗿𝗮𝘁𝗲 • Use tools like dbt Labs to standardize, model, and document data transformations into clean, trusted assets. 6️⃣ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 • API endpoints, visualization dashboards (Power BI, Tableau), or shared datasets for clients and internal teams. 🔍 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗲𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Use data products to: • Personalize customer journeys in real time • Optimize supply chains using predictive analytics • Launch new services based on market behavior insights • Drive efficiency through internal self-serve data tools 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Data collection for these products often requires accessing region-specific or protected online sources. Residential proxy infrastructure like https://lnkd.in/d82etzcQ ensures reliable, high-speed data sourcing while maintaining compliance and performance.

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 🚀 Data is no longer just a resource—it's a strategic asset. Organizations that learn to productize their data can unlock new revenue streams, drive operational efficiency, and deliver personalized user experiences at scale. At the core of this transformation are data products—modular, reusable outputs built from high-quality data that serve internal or external consumers. Think APIs, dashboards, curated datasets, ML-ready features, or embedded analytics. 📦 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 • Centralized repository (e.g., 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯, 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲) that can hold structured, semi-structured, and unstructured data at scale. 2️⃣ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 • Batch: Use Fivetran or Apache NiFi to sync data periodically • Real-Time: Stream with Apache Kafka or Amazon Kinesis 3️⃣ 𝗔𝗽𝗽𝗹𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Combine batch and stream processing for low-latency and comprehensive insights. ◦ Batch: Apache Spark ◦ Stream: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 4️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Use orchestrators like Apache Airflow or Dagster Labs to automate data flow, monitoring, and alerts. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗖𝘂𝗿𝗮𝘁𝗲 • Use tools like dbt Labs to standardize, model, and document data transformations into clean, trusted assets. 6️⃣ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 • API endpoints, visualization dashboards (Power BI, Tableau), or shared datasets for clients and internal teams. 🔍 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗲𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Use data products to: • Personalize customer journeys in real time • Optimize supply chains using predictive analytics • Launch new services based on market behavior insights • Drive efficiency through internal self-serve data tools 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Data collection for these products often requires accessing region-specific or protected online sources. Residential proxy infrastructure like https://lnkd.in/d6tAYv78 ensures reliable, high-speed data sourcing while maintaining compliance and performance.

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 🚀 Data is no longer just a resource—it's a strategic asset. Organizations that learn to productize their data can unlock new revenue streams, drive operational efficiency, and deliver personalized user experiences at scale. At the core of this transformation are data products—modular, reusable outputs built from high-quality data that serve internal or external consumers. Think APIs, dashboards, curated datasets, ML-ready features, or embedded analytics. 📦 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 • Centralized repository (e.g., 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯, 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲) that can hold structured, semi-structured, and unstructured data at scale. 2️⃣ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 • Batch: Use Fivetran or Apache NiFi to sync data periodically • Real-Time: Stream with Apache Kafka or Amazon Kinesis 3️⃣ 𝗔𝗽𝗽𝗹𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Combine batch and stream processing for low-latency and comprehensive insights. ◦ Batch: Apache Spark ◦ Stream: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 4️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Use orchestrators like Apache Airflow or Dagster Labs to automate data flow, monitoring, and alerts. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗖𝘂𝗿𝗮𝘁𝗲 • Use tools like dbt Labs to standardize, model, and document data transformations into clean, trusted assets. 6️⃣ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 • API endpoints, visualization dashboards (Power BI, Tableau), or shared datasets for clients and internal teams. 🔍 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗲𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Use data products to: • Personalize customer journeys in real time • Optimize supply chains using predictive analytics • Launch new services based on market behavior insights • Drive efficiency through internal self-serve data tools 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Data collection for these products often requires accessing region-specific or protected online sources. Residential proxy infrastructure like https://lnkd.in/dEx7AKpi ensures reliable, high-speed data sourcing while maintaining compliance and performance.

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 🚀 Data is no longer just a resource—it's a strategic asset. Organizations that learn to productize their data can unlock new revenue streams, drive operational efficiency, and deliver personalized user experiences at scale. At the core of this transformation are data products—modular, reusable outputs built from high-quality data that serve internal or external consumers. Think APIs, dashboards, curated datasets, ML-ready features, or embedded analytics. 📦 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 • Centralized repository (e.g., 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯, 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲) that can hold structured, semi-structured, and unstructured data at scale. 2️⃣ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 • Batch: Use Fivetran or Apache NiFi to sync data periodically • Real-Time: Stream with Apache Kafka or Amazon Kinesis 3️⃣ 𝗔𝗽𝗽𝗹𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Combine batch and stream processing for low-latency and comprehensive insights. ◦ Batch: Apache Spark ◦ Stream: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 4️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Use orchestrators like Apache Airflow or Dagster Labs to automate data flow, monitoring, and alerts. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗖𝘂𝗿𝗮𝘁𝗲 • Use tools like dbt Labs to standardize, model, and document data transformations into clean, trusted assets. 6️⃣ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 • API endpoints, visualization dashboards (Power BI, Tableau), or shared datasets for clients and internal teams. 🔍 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗲𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Use data products to: • Personalize customer journeys in real time • Optimize supply chains using predictive analytics • Launch new services based on market behavior insights • Drive efficiency through internal self-serve data tools 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Data collection for these products often requires accessing region-specific or protected online sources. Residential proxy infrastructure like https://lnkd.in/dQrSDVCh?utm_source=linkedin&utm_medium=organic&utm_campaign=publer ensures reliable, high-speed data sourcing while maintaining compliance and performance.

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 🚀 Data is no longer just a resource—it's a strategic asset. Organizations that learn to productize their data can unlock new revenue streams, drive operational efficiency, and deliver personalized user experiences at scale. At the core of this transformation are data products—modular, reusable outputs built from high-quality data that serve internal or external consumers. Think APIs, dashboards, curated datasets, ML-ready features, or embedded analytics. 📦 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 • Centralized repository (e.g., 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯, 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲) that can hold structured, semi-structured, and unstructured data at scale. 2️⃣ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 • Batch: Use Fivetran or Apache NiFi to sync data periodically • Real-Time: Stream with Apache Kafka or Amazon Kinesis 3️⃣ 𝗔𝗽𝗽𝗹𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Combine batch and stream processing for low-latency and comprehensive insights. ◦ Batch: Apache Spark ◦ Stream: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 4️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Use orchestrators like Apache Airflow or Dagster Labs to automate data flow, monitoring, and alerts. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗖𝘂𝗿𝗮𝘁𝗲 • Use tools like dbt Labs to standardize, model, and document data transformations into clean, trusted assets. 6️⃣ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 • API endpoints, visualization dashboards (Power BI, Tableau), or shared datasets for clients and internal teams. 🔍 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗲𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Use data products to: • Personalize customer journeys in real time • Optimize supply chains using predictive analytics • Launch new services based on market behavior insights • Drive efficiency through internal self-serve data tools 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Data collection for these products often requires accessing region-specific or protected online sources. Residential proxy infrastructure like https://lnkd.in/d82etzcQ?utm_source=linkedin&utm_medium=organic&utm_campaign=publer ensures reliable, high-speed data sourcing while maintaining compliance and performance.

🚀 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺𝗶𝗻𝗴 𝗥𝗮𝘄 𝗗𝗮𝘁𝗮 𝗶𝗻𝘁𝗼 𝗥𝗲𝘃𝗲𝗻𝘂𝗲: 𝗧𝗵𝗲 𝗣𝗼𝘄𝗲𝗿 𝗼𝗳 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 🚀 Data is no longer just a resource—it's a strategic asset. Organizations that learn to productize their data can unlock new revenue streams, drive operational efficiency, and deliver personalized user experiences at scale. At the core of this transformation are data products—modular, reusable outputs built from high-quality data that serve internal or external consumers. Think APIs, dashboards, curated datasets, ML-ready features, or embedded analytics. 📦 𝗦𝘁𝗲𝗽𝘀 𝘁𝗼 𝗕𝘂𝗶𝗹𝗱 𝗠𝗼𝗻𝗲𝘁𝗶𝘇𝗮𝗯𝗹𝗲 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀: 1️⃣ 𝗘𝘀𝘁𝗮𝗯𝗹𝗶𝘀𝗵 𝗮 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲 • Centralized repository (e.g., 𝗔𝗺𝗮𝘇𝗼𝗻 𝗦𝟯, 𝗔𝘇𝘂𝗿𝗲 𝗗𝗮𝘁𝗮 𝗟𝗮𝗸𝗲) that can hold structured, semi-structured, and unstructured data at scale. 2️⃣ 𝗜𝗻𝗴𝗲𝘀𝘁 𝗗𝗮𝘁𝗮 𝗘𝗳𝗳𝗶𝗰𝗶𝗲𝗻𝘁𝗹𝘆 • Batch: Use Fivetran or Apache NiFi to sync data periodically • Real-Time: Stream with Apache Kafka or Amazon Kinesis 3️⃣ 𝗔𝗽𝗽𝗹𝘆 𝗟𝗮𝗺𝗯𝗱𝗮 𝗔𝗿𝗰𝗵𝗶𝘁𝗲𝗰𝘁𝘂𝗿𝗲 • Combine batch and stream processing for low-latency and comprehensive insights. ◦ Batch: Apache Spark ◦ Stream: 𝗔𝗽𝗮𝗰𝗵𝗲 𝗙𝗹𝗶𝗻𝗸 4️⃣ 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝗣𝗶𝗽𝗲𝗹𝗶𝗻𝗲𝘀 • Use orchestrators like Apache Airflow or Dagster Labs to automate data flow, monitoring, and alerts. 5️⃣ 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺 & 𝗖𝘂𝗿𝗮𝘁𝗲 • Use tools like dbt Labs to standardize, model, and document data transformations into clean, trusted assets. 6️⃣ 𝗗𝗲𝗹𝗶𝘃𝗲𝗿 𝗗𝗮𝘁𝗮 𝗣𝗿𝗼𝗱𝘂𝗰𝘁𝘀 • API endpoints, visualization dashboards (Power BI, Tableau), or shared datasets for clients and internal teams. 🔍 𝗪𝗵𝗲𝗿𝗲 𝗜𝗻𝗻𝗼𝘃𝗮𝘁𝗶𝗼𝗻 𝗠𝗲𝗲𝘁𝘀 𝗔𝗽𝗽𝗹𝗶𝗰𝗮𝘁𝗶𝗼𝗻 Use data products to: • Personalize customer journeys in real time • Optimize supply chains using predictive analytics • Launch new services based on market behavior insights • Drive efficiency through internal self-serve data tools 💡 𝗣𝗿𝗼 𝗧𝗶𝗽: Data collection for these products often requires accessing region-specific or protected online sources. Residential proxy infrastructure like https://lnkd.in/dEx7AKpi?utm_source=linkedin&utm_medium=organic&utm_campaign=publer ensures reliable, high-speed data sourcing while maintaining compliance and performance.