These hold/produce data to data stores like HDFS, HBase and Hive. Both the tools have default functionality and have the ability of abstracting away the users from the complication of transferring data among these systems.
Learning Style
Learning Style
Difficulty
Course Duration
These hold/produce data to data stores like HDFS, HBase and Hive. Both the tools have default functionality and have the ability of abstracting away the users from the complication of transferring data among these systems.
Import data: Flume and Sqoop have a crucial part to play in the Hadoop ecosystem. They have the responsibility of transferring the data from sources like local file systems, HTTP, MySQL and Twitter. These hold/produce data to data stores like HDFS, HBase and Hive. Both the tools have default functionality and have the ability of abstracting away the users from the complication of transferring data among these systems.
Flume: Flume Agents have the ability to transfer data created by a streaming application to data stores like HDFS and HBase.
Sqoop: Sqoop can be used to bulk import data from typical RDBMS to Hadoop storage structures like HDFS or Hive.
Practical application for the various sources and data stores:
This course will be highly useful for those engineers who have the responsibility designing an application with HDFS/HBase/Hive as the data store. This will also be suitable for those engineers who intend to port data from legacy data stores to HDFS.
The course has a mandatory requirement of having knowledge of HDFS. You should also be having fundamental understanding of HBase and Hive shells, as HBase and Hive examples require that. Additionally, you should also be having a working installation of HDFS, because it is required to run majority of the examples.
| Subjects | Big Data |
|---|---|
| Lab Access | No |
| Learning Style | Self-Paced Learning |
| Learning Type | Course |
| Difficulty | Intermediate |
| Course Duration | 2 Hours |
| Language | English |
Tom is an innovator first, and then a Data Scientist & Software Architect. He has integrated expertise in business, product, technology and management. Tom has been involved in creating category defining new products in AI and big data for different industries, which generated more than hundred million revenue cumulatively, and served more than 10 million users.
As a Data Scientist and Software Architect Tom has extensive experience in data science, engineering, architecture and software development. To date Tom has accumulated over a decade of experience in R, Python & Linux Shell programming.
Tom has expertise on Python, SQL, and Spark. He has worked on several libraries including but not limited to Scikit-learn, Pandas, NumPy, Matplotlib, Seaborn, SciPy, NLTK, Keras, and Tensorflow.
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