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Features of the CCP DE:575 (Cloudera Hadoop and Spark Developer certification)
1. Entire syllabus will be covered.
2. All questions are scenerio based and step by step solutions will be given
3. Same will be executed by our exper technical team and complimentary selected recorded videos will be shared here. (In progress)
4. Almost all scenerios will be covered for real exams.
5. Any future updates will be free  on single and same machine.
6. Solutions are already executed on Cloudera CDH, hence same can be used for real exam.
7. Our expert regularly update the simulator.
8. It will help you gain confidence and reduce study time.
9.  Always updated and correct/incorrect  way of solutions explanation

Note : This product is tested only on Windows Operating System. IOS users are using this Wine Bottler to run exe.

Syllabus for CCP:DE575 (Data Engineer)

Data Ingest
The skills to transfer data between external systems and your cluster. This includes the following:

1. Import and export data between an external RDBMS and your cluster, including the ability to import specific subsets, change the delimiter and file format of imported data during ingest, and alter the data access pattern or privileges.
2. Ingest real-time and near-real time (NRT) streaming data into HDFS, including the ability to distribute to multiple data sources and convert data on ingest from one format to another.
3. Load data into and out of HDFS using the Hadoop File System (FS) commands.

Transform, Stage, Store
Convert a set of data values in a given format stored in HDFS into new data values and/or a new data format and write them into HDFS or Hive/HCatalog. This includes the following skills:

1. Convert data from one file format to another
2. Write your data with compression
3. Convert data from one set of values to another (e.g., Lat/Long to Postal Address using an external library)
4. Change the data format of values in a data set
5. Purge bad records from a data set, e.g., null values
6. Deduplication and merge data
7. Denormalize data from multiple disparate data sets
8. Evolve an Avro or Parquet schema
9. Partition an existing data set according to one or more partition keys
10. Tune data for optimal query performance

Data Analysis
Filter, sort, join, aggregate, and/or transform one or more data sets in a given format stored in HDFS to produce a specified result. All of these tasks may include reading from Parquet, Avro, JSON, delimited text, and natural language text. The queries will include complex data types (e.g., array, map, struct), the implementation of external libraries, partitioned data, compressed data, and require the use of metadata from Hive/HCatalog.

1. Write a query to aggregate multiple rows of data
2. Write a query to calculate aggregate statistics (e.g., average or sum)
3. Write a query to filter data
4. Write a query that produces ranked or sorted data
5. Write a query that joins multiple data sets
6. Read and/or create a Hive or an HCatalog table from existing data in HDFS

Workflow
The ability to create and execute various jobs and actions that move data towards greater value and use in a system. This includes the following skills:

1. Create and execute a linear workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom actions, etc.
2. Create and execute a branching workflow with actions that include Hadoop jobs, Hive jobs, Pig jobs, custom action, etc.
3. Orchestrate a workflow to execute regularly at predefined times, including workflows that have data dependencies

* Please read faq section carefully.

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Books on Spark or PDF to read : Machine Learning with Spark, Fast Data Processing with Spark (Second edition), Mastering Apache Spark, Learning Hadoop 2, Learning Real-time Processing with Spark Streaming, Apache Spark in Action, Apache Spark CookBook, Learning Spark, Advanced Analytics with Spark Download.

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