Apache Druid Training
Apache Druid is an open-source, real-time analytics database designed for fast queries on large datasets. It’s primarily used for OLAP (Online Analytical Processing) workloads and can ingest data from various sources like event streams, databases, and log files. It’s often used in applications where real-time data analysis is required, such as in business intelligence, monitoring, and operational analytics.
Why should you choose Nisa For Apache Druid Training?
Nisa Trainings is the best online training platform for conducting one-on-one interactive live sessions with a 1:1 student-teacher ratio. You can gain hands-on experience by working on near-real-time projects under the guidance of our experienced faculty. We support you even after the completion of the course and happy to clarify your doubts anytime. Our teaching style at Nisa Trainings is entirely hands-on. You’ll have access to our desktop screen and will be actively conducting hands-on labs on your desktop.
Job Assistance
If you face any problem while working on Apache Druid Course, then Nisa Trainings is simply a Call/Text/Email away to assist you. We offer Online Job Support for professionals to assist them and to solve their problems in real-time.
The Process we follow for our Online Job Support Service:
- We receive your inquiry for Online Job
- We will arrange a telephone call with our consultant to grasp your complete requirement and the tools you’re
- If our consultant is 100% confident in taking up your requirement and when you are also comfortable with our consultant, we will only agree to provide service. And then you have to make the payment to get the service from
- We will fix the timing for Online Job Support as mutually agreed by you and our consultant.
Course Information
Apache Druid Training
Duration: 25 Hours
Timings: Weekdays (1-2 Hours per day) [OR] Weekends (2-3 Hours per day)
Training Method: Instructor Led Online One-on-One Live Interactive
Sessions.
COURSE CONTENT :
1. Introduction to Apache Druid
- What is Druid?
- Overview of the Druid architecture
- Key features (real-time analytics, horizontal scalability, etc.)
- Druid vs Other Databases (like SQL, NoSQL, and OLAP databases)
- Use Cases for Apache Druid (e.g., monitoring, clickstream analytics, security data analysis)
2. Setting Up Apache Druid
- Installing Druid (Local setup and cluster setup)
- Configuration of Druid
- Setting up a Druid cluster (using Docker, Kubernetes, or bare-metal)
- Components of Druid
- Coordinator, Overlord, Broker, Historical, Middle Manager
- Understanding Druid’s metadata storage
- Druid on Cloud (AWS, GCP, Azure)
3. Ingesting Data into Apache Druid
- Ingestion Methods
- Batch vs Streaming data
- Real-time ingestion using Kafka
- Batch ingestion using HDFS, S3, and other file systems
- Data Format (JSON, CSV, Avro, Parquet)
- Data ingestion using the Druid Indexing Service
- Data Transformations during ingestion (e.g., filtering, aggregation)
4. Querying Data in Druid
- Druid Query Types:
GroupByqueries (for aggregating data)Timeseriesqueries (for time-based analysis)TopNqueries (for high cardinality data analysis)Scanqueries (for fast data retrieval)
- Druid SQL (Druid’s SQL-like query language)
- Writing basic and advanced SQL queries in Druid
- Druid Query Optimization (best practices for performance)
5. Data Storage and Optimization
- Segment Granularity and Partitioning
- Configuring segment sizes
- Compaction and Index Optimization
- Segment merging and partitioning strategies
- Data Retention Policies
- Managing historical data
- Sharding and Replication (for high availability and fault tolerance)
6. Druid Security
- Authentication (Kerberos, LDAP, etc.)
- Authorization (role-based access control)
- SSL/TLS encryption
- Data Masking and Encryption for sensitive information
7. Advanced Druid Features
- Real-time Data Streaming
- Integrating with Apache Kafka, Kinesis
- Druid & Machine Learning
- Using Druid for ML-based insights (e.g., anomaly detection)
- Integrating with BI Tools (Tableau, Superset, etc.)
8. Monitoring and Troubleshooting
- Monitoring Druid Cluster (using Grafana, Prometheus, etc.)
- Log analysis and debugging
- Scaling Druid for larger workloads
- Troubleshooting common issues in Druid ingestion and query processing
9. Druid in Production
- Cluster Management (Scaling up and scaling out Druid)
- Backup and Restore
- Version Upgrades in Druid
10. Case Studies & Best Practices
- Best practices for Druid deployment
- Real-world use cases and optimizations