Apache Zeppelin - Big Data Visualization Tool

Update the package list and install open JDK 11 to prepare for Apache Zeppelin, then verify the installation shows Java 11.0.27.

Set Java environment variables by editing /etc/profile to add Java home, Java path, and export lines, including the Java home and JRE path, so Java stays available.

Install Apache Zeppelin on Ubuntu by downloading Zeppelin 0.12.0, untarring the package, and starting the Zeppelin daemon. Access the web interface at localhost:8080 to create a notebook.

Learn to install Docker Desktop on Windows 10/11 by downloading the installer, running as administrator, and launching Docker to manage containers, images, volumes, and build.

Learn to run Apache Zeppelin on Docker Desktop for Windows by pulling the latest image, launching a Zeppelin container on port 8080, and starting and stopping it while checking logs.

Explore the Apache Zeppelin notebook UI, including paragraphs, interpreters, bindings, and visualization panels. Learn to run code, create charts, and schedule notebooks for collaborative data workflows.

Learn to document and structure Zeppelin notebooks with markdown, applying headers, emphasis, lists, links, images, tables, and code blocks for clear, collaborative presentations.

Learn to create, edit, and run paragraphs in Apache Zeppelin, using spark and sql interpreters to load data, run code, and visualize results with markdown.

Explore the Zeppelin user interface and learn to create and run paragraphs within notebooks for data visualization. Write markdown and code, execute cells, and manage paragraphs with cloning and deletion.

Explore dynamic forms and input controls in Apache Zeppelin to make notebooks interactive and reusable. Use text inputs, dropdowns, and number inputs to drive parameterized queries and dashboards.

Learn to create dynamic forms in Zeppelin with Spark SQL, including text input, select, and checkbox options, with default values and form templates to drive interactive data analysis.

Apache Zeppelin turns raw data into visual insights using tables, bar charts, line charts, pie charts, and scatter plot; switch from table to chart with a click.

learn the five default chart types in apache zeppelin and build interactive bar, area, line, and scatter visualizations using employee data to analyze attrition and tenure.

Interpreters connect Zeppelin to execution engines, letting you run languages like Python, Scala, SQL, or shell scripts in notebook paragraphs by prefixing the interpreter name.

Explore common interpreters in Apache Zeppelin—spark, Python (PySpark), JDBC, and shell—and learn how they connect to execution backends for flexible, mixed workflows.

Configure interpreter binding in Apache Zeppelin to control how interpreters are shared across notebooks and users, balancing performance and isolation with shared, scoped, and isolated modes.

Configure Apache Spark with Zeppelin by installing Spark, starting the Spark shell locally, and running Spark code in Zeppelin notebooks.

Configure and connect Apache Zeppelin to run Spark code interactively with the Spark interpreter, notebook style, for Spark SQL, DataFrame, and RDD workloads.

Explore how to work with RDDs and data frames in Apache Spark inside Apache Zeppelin, comparing low-level control with high-level optimized data manipulation and visualization.

Learn Spark SQL queries on a data frame in Apache Zeppelin, register a temporary view, and visualize results with built-in charts; leverage caching to speed up repeated queries.

Visualize spark outputs in Apache Zeppelin by turning spark data frames into interactive tables and charts, with percentage SQL queries and temp views to create bar, line, or pie charts.

Track Spark job performance and tune execution using Apache Zeppelin, leveraging Spark Web UI, caching, repartitioning, and broadcasting joins to optimize production data pipelines.

Apache Zeppelin connects to external data sources via Spark and JDBC interpreter, letting you ingest and explore data from HDFS, S3, and relational databases with seamless visualization.

Demonstrate connecting Apache Zeppelin to a local MySQL database via JDBC, configure the MySQL interpreter, and query the user table to fetch and view five records.

Learn how to connect Zeppelin to a remote MySQL database using a JDBC driver, configure the MySQL interpreter, and supply host, port, user id, and password for seamless querying.

Learn how to connect Apache Zeppelin to a remote MySQL database, create a database and table, insert and query data, and run sql in Zeppelin notebooks.

Learn to read csv and parquet files with Spark in Zeppelin, load, transform, and visualize data in an interactive notebook, and compare csv and parquet formats while exploring schemas.

Clean and transform a messy employee CSV file using Spark Scala in Apache Zeppelin, handling nulls and duplicates, and derive salary in k and uppercase names.

Profile data interactively in Apache Zeppelin using descriptive statistics, null checks, and distribution by category. Visualize trends with bar, pie, and line charts from grouped data.

Structure Zeppelin notebooks into modular pipeline stages for ingestion, cleaning, transformation, storage, and visualization, enabling reusable, well-documented ETL workflows and easier debugging, collaboration, and automation.

Explore how Apache Zeppelin uses %md and %angular to create structured, interactive ETL notebooks with Spark and AWS S3 CSV data, including region dropdowns for live filtering.

Parameterize pipelines in Apache Zeppelin to make ETL notebooks dynamic and reusable with inputs for date, region, and source path. Use Angular inputs to set thresholds and schedule daily runs.

Integrate Apache Zeppelin with Apache Kafka to process real time streaming data, enabling real time dashboards and alerts through Spark Streaming and Kafka interpreters.

Learn Spark MLlib basics in Zeppelin to build scalable machine learning pipelines with data loading, preprocessing, logistic regression, and model evaluation for tasks like Titanic and churn prediction.

Perform a hands-on configuration of the Hive interpreter in Apache Zeppelin by accessing localhost:11000, selecting the interpreter, and setting four key properties with username data engineer and a blank password.

Start and manage the Hadoop, Hive, and Zeppelin stack, initiate services, load notebooks, and visualize Olympics data with Zeppelin.

Learn to analyze Olympic games data with Hive integration in Apache Zeppelin, using a BI-style workflow to load, process, and graphically visualize big data insights.

Explore Python support in Apache Zeppelin, including conda docker environments, pandas querying, PySpark SQL, and Matplotlib plotting with dynamic widgets, and run Python notebooks by executing code with shift enter.

Install and run Apache Zeppelin on Ubuntu 20.04 by verifying Java, configuring JAVA_HOME, downloading and unzipping the Zeppelin binary, starting the server, and accessing the web interface on localhost.

Install the required Zeppelin components on your Linux machine and follow the steps shown in the previous lecture as part of the IBM Skills Network setup.

Log in to IBM Skills Network labs using email, GitHub, or Facebook, then access the home screen featuring Jupiter Lab and Zeppelin Notebook for your big data visualization work.

Launch and access the Zeppelin notebook in IBM Skills Network Labs by logging in, opening the Zeppelin notebook, and waiting for the Docker-backed environment to launch.

Upload and organize data in the IBM skills developer lab by creating folders, uploading files, and tracking loading progress, using sample employee data to illustrate the process.

Learn how to integrate Spark with Zeppelin for big data visualization: load data, define schemas, build data frames, run Spark SQL, and visualize results with bar and pie charts.

Learn to fetch Twitter data using API keys, load it into Apache Zeppelin for big data visualization, and analyze a Spark DataFrame to extract user locations and trends.

Conclude the Apache Zeppelin big data visualization tool course by thanking students for enrolling, expressing hope they enjoyed it, and wishing them success in their future endeavors.

Explore how Apache Spark distributes workloads across a cluster for high-performance data processing, enabling predictive analytics, machine learning, graph processing, data frames, RDDs, notebooks, and streaming.

Explore supervised learning with labeled features and a predict function, then contrast unsupervised learning and clustering, using Spark's machine learning library to train and apply predictive models.

Explore telecom customer churn prediction with Apache Zeppelin, Spark, and machine learning libraries. Build and evaluate logistic regression, Naive Bayes, and one-vs-rest models, using visualization, EDA, and Spark SQL.

Generate an access.log with the Java Apache log generator and process it with Kafka, Spark, and Apache Structured Streaming to store results in Parquet and MySQL.

Install and run a single-node Kafka setup, start zookeeper and Kafka server, create a logs topic, and load 179 records from an access log into Kafka.

Execute mysql, create and use logs db, define a logs db table with ip, timestamp, method, endpoint, status, and status_type, load and query data to prepare for the Zeppelin process.

Learn to access Kafka data with Spark streaming in Zeppelin, process logs, and write results as parquet files to the output directory while using a checkpoint for streaming state.

Join parquet to MySQL by loading processed data with Spark streaming, transferring 179 Kafka records into the logs_db table and exposing IP address, timestamp, method, endpoint, and status type.

Create a dynamic dashboard by running MySQL queries on logs db to visualize status types, client and server errors, top IPs, endpoints, and request volume over time with charts.