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Impala Assignment Help


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What is impala?


  1.  In memory, distributed SQL Query Engine
  2.  No Map/Reduce
  3.  Distributed on HDFS data nodes
  4.  Real time SQL Query Engine from Cloudera.
  5.  Build on top of Hadoop
  6.  Open source


Hadoop:

  1. Open source data storage and processing API.
  2. Hadoop is reliable and fault tolerant with no rely on hardware for these properties.
  3. It is made by apache software foundation in 2011. written in JAVA


Why impala?

  1.  Interactive data analysis
  2.  Low latency response
  3.  Deploy on existing Hadoop Clusters
  4.  Derived from the SQL syntax used in Apache Hive
  5.  Supports a subset of HiveQL statements, data types and built in functions.


Finding the Benefits of Impala online service to Impala Assignment Help:


More and faster value from "Big Data"

  1.  Interactive BI/analytics via SQL
  2.  No delays from data migration


Flexibility:

  1. Query across existing data
  2. Select best fit file formates
  3. Run multiple framework on the same data at the same time


Cost Efficiency:

  1.  Reduce movement, duplicate and compute
  2.  10% to 1% the cost of the analytic DBMS


Full Fidelity Analysis:

  1.  No loss from aggregations or fixed schemas


Impala Features:

Data Agility: 1) available to any app/ framework (SQL, Spark, MapReduce, S3 applications, etc.)

2) Direct schema on read. No need to load data into Impala.

3) Users can query data across S3, HDFS, Kudu, HBase, etc.


Scalability: 1) No hard limits and scales with cluster size

2) No hard limits; deployments can exceed 100s of nodes


Compatibility: 1) support integration with most tools

2) support updatable data with Apache Kudu

3) No support for OLAP and SET functions


Elasticity: 1) Data can be queried directly from S3, with no data load operations required

2) Adds/removes nodes incrementally on demand


Apache Impala vs Hive:



Impala
Hive
system type
Interactive
Batch processing
Resource Utilization
Aggressive
Conservative
Predictable time   completion
Not always.Generally very fast, but can degenerate in queries with lots of joins
Yes. almost linear. works well with complex queries
Allow code injection
no, does not rely on   Map/Reduce
Yes. injection of M/R functions with Hive streaming
User defined functions
Yes
Yes


Example Schema:


CREATE TABLE Customers {
id BIGINT,
address STRUCT <
city: STRING,
Zip: INT
>
orders ARRAY <STRUCT<
txn_time: TIMESTAMP,
cc: BIGINT,
items: ARRAY<STRUCT<
item_no: STRING,
price: DECIMAL(9, 2)
>>>>
preferred_c BIGINT
}


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