Introduction to Streamsets Training:
Our Streamsets Training is an open source, enterprise-grade, continuous big data ingest infrastructure that accelerates time to analysis by bringing unprecedented transparency and processing to data in motion. For more information register with us or dial our helpline to find best training guides for Streamsets Corporate Training and Streamsets Classroom Training and become a better executive. IdesTrainings is one of the best IT Training delivering Partners, we can gather up profound trainers for all the possible latest technologies at Hyderabad, Bangalore, Pune, Gurgaon and other such IT hubs. Call now for Streamsets online are corporate training, our team members will reach you.
Prerequisites for Streamsets training:
Students preferably should have a general knowledge of operating systems, networking, programming concepts, and databases.
Streamsets Corporate Training Course Outline:
- Course Name: Streamsets Training
- Duration of the Course: 40 Hours (It can also be optimized as per required period).
- Mode of Training: Classroom and Corporate Training
- Timings: According to one’s Feasibility
- Materials: Yes, We are providing Materials for Streamsets Corporate Training (We will get the soft copy material)
- Sessions will be conducted through WEBEX, GOTOMETTING or SKYPE
- Basic Requirements: Good Internet Speed, Headset.
- Trainer Experience: 10+Years
- Course Fee: Please register in our website, so that one of our agents will assist you
Streamsets Online Training Course Content:
Module1: Overview of the StreamSets
1.1 DataOps Platform
1.2 DataOps Platform Overview
1.3 StreamSets DataOps Architecture and Use Cases
1.4 Custom Examples
Module2: StreamSets Data Collector Introduction
2.1 Getting Started with Data Collector
2.2 SDC Overview
2.3 Building Pipelines
2.4 Previewing Data
2.5 Running the Pipeline
Module3: Development of Pipeline
3.1 Connectors
3.2 Processors & Evaluators
3.3 Executors
3.4vExpression Language
Module4: Pipeline Events, Rules, and Alerts
4.1 Generating and Handling Events
4.2 Metric Rules
4.3 Data Rules
Module5: Reading, Writing and Transforming Data
5.1 Flat Files
5.2 RDBMS: MySQL, Oracle, and Change Data Capture
5.3 Messaging Broker Systems: Kafka
5.4 Event Based: APIs
5.5 Distributed Storage: HDFS
5.6 Lookups: Relational Databases
Module6: Controlling and Tracking
6.1 Maintenance of SDC instances
Module7: Overview of the StreamSets
7.1 Data Operations
7.2 Data Operations Platform Overview
7.3 StreamSets Control Hub Use Cases
Module8: Establishment of StreamSets
8.1 Control Hub
8.2 The Motivation for SCH
8.3 Key SCH Features
8.4 Deployment Methods
8.5 The SCH Architecture
Module9: Getting Started With Control Hub
9.1 SCH Overview
9.2 The SCH User Interface
9.3 Overview Pipelines, Jobs, Topologies
9.4 Managing Data Collectors
9.5 Operational Management
Module10: Configuration
10.1 SCH Configuration
10.1 Organizations and Users Roles and Groups
10.2 Sharing Objects Between Users
Module11: Functioning of Data Collectors
11.1 Registering SDC Instances with SCH
11.2 Using Labels
Module12: Operating of Pipelines
12.1 The Pipeline Repository
12.2 Creating and Editing Pipelines in SCH
Module13: Managing Jobs
13.1 Creating and Running Jobs
13.2 Scheduling Job
Module14: Administration and Monitoring
14.1 Tracking your SCH instance and your Data Platform
Module15: High Availability
15.1 High Accessibility of Pipelines
15.2 High Accessibility of the Platform
Module16: Overview of the StreamSets Data Operations Platform
16.1 DataOps Platform Overview
16.2 StreamSets DataOps Architecture and Use Cases
Module17: Transformer UI Overview
17.1 Pipelines
17.2 Controls & Views
17.3 Package Management
17.4 Origins, Operators, Destinations
Module18: Overview of Spark
18.1 Spark Overview
18.2 RDDS
18.3 DataFrames
18.4 Datasets
Module19: Transformer Deep Dive
19.1 Transformer Execution
19.2 Pipeline Processing on Spark
19.3 Transformer Batch Mode
19.4 Transformer Streaming Mode
19.5 Data Origin &Data Sources
19.6 Spark Partitioning & Caching
19.7 Ludicrous Mode
Module20: Batch Processing
20.1 Spark Batch Processing
20.2 Transformer Batch Processors
20.3 SparkSQL
Module21: Logs& Monitoring
21.1 Log Management& log files
21.2 Monitoring Pipelines
21.3 Spark UI & Execution
Module22: Framework Connectors
22.1 Hadoop Distributed Architecture
22.2 Hadoop, Hive, Kafka, spark, Databricks, Snowflakes, AWS, and Azure Operators
22.3 Hive Tables
Overview of Streamsets:
- A key step in modernizing your data processing architecture is to upgrade how you move data from logs, IoT sensors, and other sources to your enterprise data hub. An integrated solution combining StreamSets with Cloudera Enterprise makes it possible to continually feed your analytics applications consumption-ready data with efficiency, operational control, and agility.
- StreamSets deploys via a Cloudera Manager parcel onto your cluster. It provides a full-featured, integrated development environment (IDE) that lets you build, execute and operate any-to-any ingest pipelines that mesh stream and batch data, and include a variety of in-stream transformations—all without having to write custom code. StreamSets lets you build data flows with direct integration to numerous Cloudera Enterprise components including HDFS, Kafka, Solr, Hive, HBASE, Impala, CDSW, Kudu, and Cloudera Navigator.
- Once StreamSets is running, you get real-time monitoring for both data anomalies and data flow operations, including threshold-based alerting, anomaly detection, and automatic remediation of error records. Because it is architected to logically isolate each stage in a pipeline, you can meet new business requirements by dropping in new processors and connectors without code and with minimal downtime.
What is Streamsets?
StreamSets is a cloud native collection of products designed to control data drift: the problem of changes in data, data sources, data infrastructure, and data processing. The company calls its applications a data operations platform. Included features are a living data map, performance management indices, and smart pipelines providing a similar level of control to common business operations systems.
StreamSets Data Collector (SDC):
The SDC is the workhorse of the system which implements your data plane, i.e. the actual physical movement of data from one place to another. It provides a data pipeline authoring environment that helps you build any-to-any data movement pipelines using a drag-and-drop graphical interface or programmatically using Python or Java. The pipelines have the capability to work with minimal or no schema/structure specification and can filter, decorate or transform data as it flows through. Here is a screenshot of what a running pipeline may look like in SDC.
These pipelines can run in standalone mode, cluster streaming mode, or cluster batch mode. The SDC which runs these pipelines can be installed on free standing dedicated nodes or edge/gateway/cluster nodes alike. All that is needed is that SDC has direct access to the data sources and destinations it is operating on, and sufficient resources to run the dataflow.
The SDC is distributed as a rpm, tar-ball, Cloudera parcel, Docker image, and custom VM for various cloud environments.
How can I use StreamSets?
You can begin using StreamSets by installing an SDC on a supported system, or spin it up from Docker Hub, or install it through Cloudera Manager etc. Once an SDC is up and running, you can create pipelines that move data from your data sources to desired destination systems. The SDC in and of itself is fully capable of running continuous dataflows in a secure and manageable manner. However, if you do find yourself using more than one pipeline, it would be useful to connect all your SDC instances to a DPM and use that as your operations hub for all dataflows.
Conclusion to Streamsets Training:
IdesTrainings makes you an expert in all the concepts of Streamsets and also possible Streamsets Concepts. Get a fully-fledged Streamsets Corporate Course training for a better view and understanding. At IdesTrainings, it is a matter of pride for us to make job oriented hands on courses available to anyone, anytime and anywhere. Therefore we ensure that you can enroll in the course 24 hours a day, seven days a week, and 365 days a year. Learn at a time and place, and pace that is of your choice. If you have any doubts regarding the Streamsets Online Training or job support, always feel free to contact us or you can also register with us so that one of our coordinators will contact you as soon as possible. Our team is available round the clock. We provide Streamsets corporate training also Classroom Training at Hyderabad, Bangalore, Chennai, Noida, Delhi, Mumbai, Kolkata and other possible places and cities.
Frequently Asked Questions (FAQ's):
1.Is StreamSets an Opensource?
Under Apache License 2.0 StreamSets data collector is an opensource, enterprise-grade, continuous big data ingest infrastructure that accelerates time to analysis by bringing unprecedented transparency and processing to data in motion.
2.Can we use StreamSets for free?
Yes, its free and easy to get started. All you need is to sign up for free and log into the StreamSets DataOps Platform. Depending upon the type of environment, you can set up the Data Collector Engine.
3.What does a StreamSets Data Collector do?
StreamSets Data Collector enables the reading of data from an edge device or receiving the data from another data flow pipeline. Messaging protocols like HTTP, MQTT, CoAP, and web sockets are supported.
4.What is the function of StreamSets data collector?
The SDC is the Workhorse of the system which implements your data plane and is able to provide data pipeline authoring environment that helps you build any-to-any data movement pipelines using a drag-and-drop graphical interface.
5.What is the difference between Kafka and StreamSets?
With a unique design Kafka provides messaging system’s functionality and for full life-cycle management of data in motion StreamSets is the industry’s first data operations platform. StreamSets comes under Data Science Tools while Kafka comes under the category of message queue tool.