Big Data Analytics Tools

Not all AI agents are created equal — and the framework you choose shapes your system's intelligence, adaptability, and real-world value. As we transition from monolithic LLM apps to 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝘀𝘆𝘀𝘁𝗲𝗺𝘀, developers and organizations are seeking frameworks that can support 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹 𝗿𝗲𝗮𝘀𝗼𝗻𝗶𝗻𝗴, 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝘃𝗲 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗶𝗻𝗴, and 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝗼𝘂𝘀 𝘁𝗮𝘀𝗸 𝗲𝘅𝗲𝗰𝘂𝘁𝗶𝗼𝗻. I created this 𝗔𝗜 𝗔𝗴𝗲𝗻𝘁𝘀 𝗙𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗖𝗼𝗺𝗽𝗮𝗿𝗶𝘀𝗼𝗻 to help you navigate the rapidly growing ecosystem. It outlines the 𝗳𝗲𝗮𝘁𝘂𝗿𝗲𝘀, 𝘀𝘁𝗿𝗲𝗻𝗴𝘁𝗵𝘀, 𝗮𝗻𝗱 𝗶𝗱𝗲𝗮𝗹 𝘂𝘀𝗲 𝗰𝗮𝘀𝗲𝘀 of the leading platforms — including LangChain, LangGraph, AutoGen, Semantic Kernel, CrewAI, and more. Here’s what stood out during my analysis: ↳ 𝗟𝗮𝗻𝗴𝗚𝗿𝗮𝗽𝗵 is emerging as the go-to for 𝘀𝘁𝗮𝘁𝗲𝗳𝘂𝗹, 𝗺𝘂𝗹𝘁𝗶-𝗮𝗴𝗲𝗻𝘁 𝗼𝗿𝗰𝗵𝗲𝘀𝘁𝗿𝗮𝘁𝗶𝗼𝗻 — perfect for self-improving, traceable AI pipelines.  ↳ 𝗖𝗿𝗲𝘄𝗔𝗜 stands out for 𝘁𝗲𝗮𝗺-𝗯𝗮𝘀𝗲𝗱 𝗮𝗴𝗲𝗻𝘁 𝗰𝗼𝗹𝗹𝗮𝗯𝗼𝗿𝗮𝘁𝗶𝗼𝗻, useful in project management, healthcare, and creative strategy.  ↳ 𝗠𝗶𝗰𝗿𝗼𝘀𝗼𝗳𝘁 𝗦𝗲𝗺𝗮𝗻𝘁𝗶𝗰 𝗞𝗲𝗿𝗻𝗲𝗹 quietly brings 𝗲𝗻𝘁𝗲𝗿𝗽𝗿𝗶𝘀𝗲-𝗴𝗿𝗮𝗱𝗲 𝘀𝗲𝗰𝘂𝗿𝗶𝘁𝘆 𝗮𝗻𝗱 𝗰𝗼𝗺𝗽𝗹𝗶𝗮𝗻𝗰𝗲 to the agent conversation — a key need for regulated industries.    ↳ 𝗔𝘂𝘁𝗼𝗚𝗲𝗻 simplifies the build-out of 𝗰𝗼𝗻𝘃𝗲𝗿𝘀𝗮𝘁𝗶𝗼𝗻𝗮𝗹 𝗮𝗴𝗲𝗻𝘁𝘀 𝗮𝗻𝗱 𝗱𝗲𝗰𝗶𝘀𝗶𝗼𝗻-𝗺𝗮𝗸𝗲𝗿𝘀 through robust context handling and custom roles.  ↳ 𝗦𝗺𝗼𝗹𝗔𝗴𝗲𝗻𝘁𝘀 is refreshingly light — ideal for 𝗿𝗮𝗽𝗶𝗱 𝗽𝗿𝗼𝘁𝗼𝘁𝘆𝗽𝗶𝗻𝗴 𝗮𝗻𝗱 𝘀𝗺𝗮𝗹𝗹-𝗳𝗼𝗼𝘁𝗽𝗿𝗶𝗻𝘁 𝗱𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁𝘀.  ↳ 𝗔𝘂𝘁𝗼𝗚𝗣𝗧 continues to shine as a sandbox for 𝗴𝗼𝗮𝗹-𝗱𝗿𝗶𝘃𝗲𝗻 𝗮𝘂𝘁𝗼𝗻𝗼𝗺𝘆 and open experimentation. 𝗖𝗵𝗼𝗼𝘀𝗶𝗻𝗴 𝘁𝗵𝗲 𝗿𝗶𝗴𝗵𝘁 𝗳𝗿𝗮𝗺𝗲𝘄𝗼𝗿𝗸 𝗶𝘀𝗻’𝘁 𝗮𝗯𝗼𝘂𝘁 𝗵𝘆𝗽𝗲 — 𝗶𝘁’𝘀 𝗮𝗯𝗼𝘂𝘁 𝗮𝗹𝗶𝗴𝗻𝗺𝗲𝗻𝘁 𝘄𝗶𝘁𝗵 𝘆𝗼𝘂𝗿 𝗴𝗼𝗮𝗹𝘀: - Are you building enterprise software with strict compliance needs?   - Do you need agents to collaborate like cross-functional teams?   - Are you optimizing for memory, modularity, or speed to market? This visual guide is built to help you and your team 𝗰𝗵𝗼𝗼𝘀𝗲 𝘄𝗶𝘁𝗵 𝗰𝗹𝗮𝗿𝗶𝘁𝘆. Curious what you're building — and which framework you're betting on?

dbt – free Kafka – free Spark – free Airflow – free Docker – free Parquet – free VS Code – free Postgres – free Superset – free AWS Free Tier – free Even the best open-source notebooks and data viz tools? Free. With a laptop and solid Wi-Fi, you can build a lot of leverage today. Every tool you need to become a top 1% data engineer is already out there. Every concept you need to learn, schemas, pipelines, orchestration, streaming, batch, SQL optimization, it’s on GitHub, in the docs, in open courses, waiting for someone willing to break stuff and build again. Nobody is stopping you from launching your first end-to-end pipeline, from joining the DataEngineering community, from reading warehouse benchmarks, or open-source PRs. You can deploy a large-scale warehouse on a free tier, learn distributed joins and shuffles on your own laptop, practice partitioning, build data lakes, automate with Python scripts and see exactly how the world runs behind the scenes. Don’t wait for the “perfect project” or a certificate. Don’t tell yourself you need permission, or a course, or someone’s LinkedIn thread to validate your skills. The tools are there. The docs are there. The community is there. What are you waiting for? Go build.

CISOs, you're likely spending more on Splunk or Elastic than you're comfortable admitting? You’re not alone. I've recently spoken to many SOC leaders who felt almost helpless at their SIEM bills (primarily because they will never replace their legacy SIEMs because of the cost of switching, features and integrations etc.). The story around next-gen SIEM is for another day..... Regardless of your SIEM deployment, we know across the industry, security teams are facing a common pain: growing data volumes → rising Splunk bills → limited visibility due to cost-driven ingestion filters. But there’s a fix. The smartest SOC leaders are now deploying Security Data Pipeline Platforms (SDPPs) solutions purpose-built to optimize, enrich, and route security telemetry before it hits destination SIEMs. Essentially, helping you get the best out of your Splunk, Elastic or Sentinel SIEMs etc. These solutions help: ▪️ Reduce data sources and ingestion volume ▪️ Filter out noise, and enrich critical signals for alerts ▪️ Centralized policy management: Define routing, filtering, masking, and enrichment rules once and apply across multiple destinations (e.g., Splunk, S3, Snowflake, etc.). Then makes it easy to route to lower-cost destinations (SIEM + data lake + cold storage) ▪️ Improved visibility & troubleshooting for data observability: Track dropped logs, schema errors, misrouted data, or delayed ingestion with a real-time view of data flow health ▪️ PII Redaction / Masking: Redact sensitive fields before logs reach third-party analytics tools, ensuring privacy compliance (e.g., GDPR, HIPAA). And much more...... (I outline them in my report below) This new class of data pipeline vendors help extend the life of your SIEM, ie, not replace it, but better leverage it. There are many solutions on the market, but in our research piece, we go super in-depth into some of the leading vendors on the market as case studies into the overall market: ✔️ Cribl ✔️ Abstract Security ✔️ Onum ✔️ VirtualMetric ✔️ Monad ✔️ DataBahn.ai ✔️ Datadog ✔️ Stellar Cyber ➕ There is a longer list in the market map, but every leader should look at these solutions first. TLDR:The ROI/cost savings I've heard for those using SDPP (especially if you're using a legacy SIEM) is mindblowing based on the numbers I've heard from SOC leaders using one of these solutions above or below. In my opinion, if you’re using any old SIEM without a telemetry pipeline, you’re likely paying for noise, lots of extra bills, and honestly, it feels like a no-brainer..... And worse, you're likely not filtering correctly for the context your SOC actually needs to do good threat hunting/compliance reporting etc. 🔗 I published a full market guide on everything here: https://lnkd.in/gYfKwYCA *** If you're a SOC leader, feel free to DM on any of the solutions. Would love your thoughts as well — what tools are helping you balance cost and signal?

♐️Apache Spark is to data engineers as SQL is to database administrators. Just as database administrators leverage SQL to access, manage and query relational databases, data engineers emphasize using Apache Spark as a multipurpose tool for large-scale data processing. Spark enables data engineers to clean, extract, transform and analyze massive datasets using SQL, streaming, machine learning and graph processing capabilities all within a unified framework. Like SQL for relational databases, Spark provides data engineers a common interface for data wrangling across a variety of workloads. The platform handles low-level parallelization, distributed computing and fault tolerance behind the scenes, allowing data engineers to focus on data problems rather than infrastructure. Spark has a number of features that make it well-suited for big data processing, including:- ✅In-memory processing: Spark stores data in memory, which makes it much faster than traditional disk-based systems. ✅Resilient Distributed Datasets (RDDs): Spark uses RDDs to distribute data across a cluster of computers, which makes it easy to parallelize data processing tasks. ✅Efficient execution: Spark has a number of optimization techniques that make it efficient at processing large datasets, such as pipelining and data compression. ✅It can support a wide range of data sources: can read data from a variety of sources, including HDFS, HBase, Cassandra, and more. ✅Multiple APIs: Spark offers APIs in Scala, Python, R, and SQL, making it easy to use with a wide range of data processing tasks. Sharing few resources to learn spark for free - Here's a set of insightful resources to learn Spark: - Get started with Apache Spark - https://lnkd.in/d8bqkiGa - Spark Starter Kit free course on Udemy - https://lnkd.in/gdSSWmws - PySpark with Krish Naik - https://lnkd.in/dNqwptBA - Get your hands dirty with SparkByExamples - https://lnkd.in/di87FHcU - Apache Spark tutorial by Databricks - https://lnkd.in/gaUZqNm5 - Explore PySpark projects with Alex Ioannides - https://lnkd.in/dxhYZMJG - Tune and optimize Spark Jobs - https://lnkd.in/dA5yPmgG - Build game-changing data-driven apps by integrating MongoDB and PySpark by Aashay Patil - http://bit.ly/42iM2xC - Prepare for interviews with Apache spark reference - https://lnkd.in/dwb4CDjr - Hands-on Apache Spark using Python with Wenqiang Feng, Ph.D. on GitHub - https://lnkd.in/d2X9ecJQ - 10 min. Spark introduction by Darshil Parmar - https://lnkd.in/gKB9gTbJ 👉Here's another interesting article on Magnet: A scalable and performant shuffle architecture for Apache Spark by LinkedIn (Min Shen) - https://lnkd.in/gKxcb-_n. #bigdata #engineering #dataanalytics #data #python #spark #cloud #dataengineering #sql #analytics #pyspark #datamining

How do you bring GIS, BIM, and CAD data into a single usable system? We know the real power lies beyond visualisation. We talk constantly about integrating diverse datasets to build powerful digital twins. One indispensable tool in the expert's kit is FME - Feature Manipulation Engine. Think of it as the universal transformation powerhouse for spatial data. FME shines at the critical ETL. The Extract, Transform and Load stage. 1️⃣It extracts data from hundreds of formats like Esri Geodatabases, Revit via IFC, AutoCAD, point clouds, databases or APIs. 2️⃣It transforms that data into a unifying Coordinate Reference System (CRS), simplifying complex geometries for real-time performance and mapping attributes. 3️⃣It loads the results into engine-ready formats like FBX or glTF, or platforms like Unreal Engine and Unity. Mastering data integration is fundamental for intelligent digital twins 🌍 Make FME your data conversion "Swiss Army Knife". If you find this helpful... ----------- Follow Me for #digitaltwins Links in My Profile Florian Huemer

Data architect is the next step after data engineer on the technical ladder. What big questions should you be able to answer as a data architect? - should our pipelines be streaming or batch? Having a firm understanding of the trade offs of lambda (streaming + batch) versus kappa (streaming only) architecture is a key thing to being a great data architect. - how should our master data be modeled? This bucket is complex and has a few competing ideologies between Kimball data modeling, Inmon data modeling and one big table (OBT) data modeling. Each of these ideologies have trade offs that are too long to discuss in this LinkedIn post. - what data stores should we use for serving our data? Technology selection is another critical component. Betting everything on Snowflake or Spark is a losing battle. Understanding low latency stores like Druid, Memcached and Redis will serve you well. Also know analytical DBs like CouchDB and DuckDB. - how do we create processes to ensure data quality across all our pipelines Processes like spec review, design discussions, and data validation will greatly level up your data. As a data architect you should be flexing your leadership skills to get these adopted across your company. What other skills should a data architect know? #dataengineering

Data cleaning is a challenging task. Make it less tedious with Python! Here’s how to use Python to turn messy data into insights: 1. 𝗦𝘁𝗮𝗿𝘁 𝘄𝗶𝘁𝗵 𝗣𝗮𝗻𝗱𝗮𝘀: Pandas is your go-to library for data manipulation. Use it to load data, handle missing values, and perform transformations. Its simple syntax makes complex tasks easier.     2. 𝗛𝗮𝗻𝗱𝗹𝗲 𝗠𝗶𝘀𝘀𝗶𝗻𝗴 𝗗𝗮𝘁𝗮: Use Pandas functions like isnull(), fillna(), and dropna() to identify and manage missing values. Decide whether to fill gaps, interpolate data, or remove incomplete rows.     3. 𝗡𝗼𝗿𝗺𝗮𝗹𝗶𝘇𝗲 𝗮𝗻𝗱 𝗧𝗿𝗮𝗻𝘀𝗳𝗼𝗿𝗺: Clean up inconsistent data formats using Pandas and NumPy. Functions like str.lower(), pd.to_datetime(), and apply() help standardize and transform data efficiently.     4. 𝗗𝗲𝘁𝗲𝗰𝘁 𝗮𝗻𝗱 𝗥𝗲𝗺𝗼𝘃𝗲 𝗗𝘂𝗽𝗹𝗶𝗰𝗮𝘁𝗲𝘀: Ensure data integrity by removing duplicates with Pandas drop_duplicates() function. Identify unique records and maintain clean datasets.     5. 𝗥𝗲𝗴𝗲𝘅 𝗳𝗼𝗿 𝗧𝗲𝘅𝘁 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴: Use regular expressions (regex) to clean and standardize text data. Python’s re library and Pandas str.replace() function are perfect for removing unwanted characters and patterns.     6. 𝗔𝘂𝘁𝗼𝗺𝗮𝘁𝗲 𝘄𝗶𝘁𝗵 𝗦𝗰𝗿𝗶𝗽𝘁𝘀: Write Python scripts to automate repetitive cleaning tasks. Automation saves time and ensures consistency across your data-cleaning processes.     7. 𝗩𝗮𝗹𝗶𝗱𝗮𝘁𝗲 𝗬𝗼𝘂𝗿 𝗗𝗮𝘁𝗮: Always validate your cleaned data. Check for consistency and completeness. Use descriptive statistics and visualizations to confirm your data is ready for analysis.     8. 𝗗𝗼𝗰𝘂𝗺𝗲𝗻𝘁 𝗬𝗼𝘂𝗿 𝗖𝗹𝗲𝗮𝗻𝗶𝗻𝗴 𝗣𝗿𝗼𝗰𝗲𝘀𝘀: Keeping detailed records helps maintain transparency and allows others to understand your steps and reasoning. By using Python for data cleaning, you’ll enhance your efficiency, ensure data quality, and generate accurate insights. How do you handle data cleaning in your projects? ---------------- ♻️ Share if you find this post useful ➕ Follow for more daily insights on how to grow your career in the data field #dataanalytics #datascience #python #datacleaning #careergrowth

Best LLM-based Open-Source tool for Data Visualization, non-tech friendly CanvasXpress is a JavaScript library with built-in LLM and copilot features. This means users can chat with the LLM directly, with no code needed. It also works from visualizations in a web page, R, or Python. It’s funny how I came across this tool first and only later realized it was built by someone I know—Isaac Neuhaus. I called Isaac, of course: This tool was originally built internally for the company he works for and designed to analyze genomics and research data, which requires the tool to meet high-level reliability and accuracy. ➡️Link https://lnkd.in/gk5y_h7W As an open-source tool, it's very powerful and worth exploring. Here are some of its features that stand out the most to me: 𝐀𝐮𝐭𝐨𝐦𝐚𝐭𝐢𝐜 𝐆𝐫𝐚𝐩𝐡 𝐋𝐢𝐧𝐤𝐢𝐧𝐠: Visualizations on the same page are automatically connected. Selecting data points in one graph highlights them in other graphs. No extra code is needed. 𝐏𝐨𝐰𝐞𝐫𝐟𝐮𝐥 𝐓𝐨𝐨𝐥𝐬 𝐟𝐨𝐫 𝐂𝐮𝐬𝐭𝐨𝐦𝐢𝐳𝐚𝐭𝐢𝐨𝐧: - Filtering data like in Spotfire. - An interactive data table for exploring datasets. - A detailed customizer designed for end users. 𝐀𝐝𝐯𝐚𝐧𝐜𝐞𝐝 𝐀𝐮𝐝𝐢𝐭 𝐓𝐫𝐚𝐢𝐥: Tracks every customization and keeps a detailed record. (This feature stands out compared to other open-source tools that I've tried.) ➡️Explore it here: https://lnkd.in/gk5y_h7W Isaac's team has also published this tool in a peer-reviewed journal and is working on publishing its LLM capabilities. #datascience #datavisualization #programming #datanalysis #opensource

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

Make your #dataengineering journey easier by learning #apachespark effectively. Here is a structured approach to learning Apache Spark. You can choose to learn Spark with either #Scala or #Python, but I recommend Python because it's easier to learn. Previously, I shared roadmaps for Python, SQL, and AWS. Now, it's time for Apache Spark. Follow this guide to get started: 𝐁𝐚𝐬𝐢𝐜 Introduction to Apache Spark ✔ Understand what Apache Spark is and why it is used. ✔ Learn about the core components of Spark: Spark Core, Spark SQL, Spark Streaming, Spark MLlib, and GraphX. ✔ Explore the benefits of using Spark for big data processing. Setting Up Spark ✔ Install Apache Spark on your local machine. ✔ Understand the different deployment modes (local, standalone, on YARN, on Mesos). Spark Architecture ✔ Learn about the architecture of Spark: Driver, Executors, and Cluster Manager. ✔ Understand how Spark processes data using RDDs (Resilient Distributed Datasets) and DAG (Directed Acyclic Graph). Basic Operations with RDDs ✔ Create RDDs from collections and external data sources. ✔ Perform basic transformations (map, filter, flatMap) and actions (collect, count, reduce) on RDDs. 𝐈𝐧𝐭𝐞𝐫𝐦𝐞𝐝𝐢𝐚𝐭𝐞 Spark SQL and DataFrames ✔ Learn about Spark SQL and its role in processing structured data. ✔ Work with DataFrames and understand their benefits over RDDs. ✔ Perform SQL queries on DataFrames using Spark SQL. Data Sources ✔ Read data from various sources (CSV, JSON, Parquet, etc.) and write data back to these formats. ✔ Work with Hive tables and understand how Spark integrates with Hive. Basic Performance Tuning ✔ Understand Spark's execution plan. ✔ Learn about caching and persistence to optimize Spark jobs. ✔ Explore basic performance tuning techniques. 𝐒𝐮𝐠𝐠𝐞𝐬𝐭𝐞𝐝 𝐋𝐞𝐚𝐫𝐧𝐢𝐧𝐠 𝐏𝐚𝐭𝐡: 🔹 Start with the basics: Familiarize yourself with Spark's architecture and core concepts. Set up Spark on your local machine and perform basic RDD operations. Databricks community edition can be used as it's totally free with limitation 🔹 Move to intermediate topics: Learn how to use Spark SQL and DataFrames for structured data processing. Understand how to read from and write to various data sources. 🔹 Practice with projects: Implement small projects to reinforce your learning and gain hands-on experience. 𝐓𝐢𝐩𝐬: 🔹 Practice regularly: Work on small projects or problems to reinforce your learning. 🔹 Join the community: Participate in Spark forums and communities to stay updated and seek help when needed. 🔹 Experiment and explore: Don't be afraid to experiment with different features and functionalities of Spark to gain a deeper understanding. This roadmap should help you get started with Apache Spark and build a solid foundation for your data engineering journey. Image credit: nexocode 🤝 Stay Active Nishant Kumar