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14 Top Data Pipeline Key Terms Explained

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 Here are some key terms commonly used in data pipelines 1. Data Sources Definition: Points where data originates (e.g., databases, APIs, files, IoT devices). Examples: Relational databases (PostgreSQL, MySQL), APIs, cloud storage (S3), streaming data (Kafka), and on-premise systems. 2. Data Ingestion Definition: The process of importing or collecting raw data from various sources into a system for processing or storage. Methods: Batch ingestion, real-time/streaming ingestion. 3. Data Transformation Definition: Modifying, cleaning, or enriching data to make it usable for analysis or storage. Examples: Data cleaning (removing duplicates, fixing missing values). Data enrichment (joining with other data sources). ETL (Extract, Transform, Load). ELT (Extract, Load, Transform). 4. Data Storage Definition: Locations where data is stored after ingestion and transformation. Types: Data Lakes: Store raw, unstructured, or semi-structured data (e.g., S3, Azure Data Lake). Data Warehous...
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Cloudera Impala top features useful for developers

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Cloudera Impala that runs on Apache Hadoop. The program was proclaimed in October 2012 with a common beta trial dispersion. Popular usage is in data analytics.The key features useful for interviews.
Impala The Apache-licensed Impala program begets scalable collateral database techniques to Hadoop, authorizing consumers to subject low-latency SQL requests to information kept in HDFS and Apache HBase short of needing information motion either alteration.


Impala is amalgamated with Hadoop to employ the similar file and information setups, metadata, safeguarding and asset administration architectures applied by MapReduce, Apache Hive, Apache Pig and different Hadoop code.

Impala Applications

Impala is advanced for experts and information experts in science to accomplish systematic computational analysis of data or statistics on information kept in Hadoop through SQL either trade intellect implements. 

 
The effect is that extensive information handling (via MapReduce) and two-way requests may be completed on the similar configuration utilizing the similar information and metadata – eliminating the demand to wander information places in to specific setups and or exclusive setups plainly to accomplish examination. 


Features included
  • Supports HDFS#Hadoop_distributed_file_system|HDFS and Apache HBase storage
  • Reads Hadoop date setups, containing written material, LZO, SequenceFile, Avro and RCFile Supports Hadoop safeguarding (Kerberos authentication)
  • Fine-grained, Role-based allowance with Sentry Uses metadata, ODBC driver, and SQL structure as of Apache Hive

In first 2013, a column-oriented DBMS|column-oriented information setup named Parquet was proclaimed for designs containing Impala. In December 2013, Amazon Web Services proclaimed aid aimed at Impala.


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