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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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Big Data: IBM InfoSphere BigInsights Basics

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I am explaining here why you need IBM infoSphere. You all know about what is file system in Hadoop.
Hadoop is a distributed file system and data processing engine that is designed to handle extremely high volumes of data in any structure.
In simpler terms, just imagine that you've got dozens, or even hundreds (or thousands!) of individual computers racked and networked together. Each computer (often referred to as a node in Hadoop-speak) has its own processors and a dozen or so 2TB or 3TB hard disk drives.

All of these nodes are running software that unifies them into a single cluster, where, instead of seeing the individual computers, you see an extremely large volume where you can store your data.

The beauty of this Hadoop system is that you can store anything in this space: millions of digital image scans of mortgage contracts, days and weeks of security camera footage, trillions of sensor-generated log records, or all of the operator transcription notes from a call center. 

This ingestion of data, without worrying about the data model, is actually a key tenet of the NoSQL movement.

IBM InfoSphere BigInsights


BigInsights features Apache Hadoop and its related open source projects as a core component. This is informally known as the IBM Distribution for Hadoop. IBM remains committed to the integrity of these open source projects and will ensure 100 percent compatibility with them.
BigInsights is IBM Open Source for Hadoop
This fidelity to open source provides a number of benefits. For people who have developed code against other 100 percent open source–compatible distributions, their applications will also run on BigInsights, and vice versa. This open source compatibility has enabled IBM to amass over 100 partners, including dozens of software vendors, for BigInsights.

Simply put, if the software vendor uses the libraries and interfaces for open source Hadoop, they'll work with BigInsights as well.

Components in IBM Infosphere Biginsights

Hadoop (common utilities, HDFS, and the MapReduce framework)

1.0.3

Avro (data serialization)

1.6.3

Chukwa (monitoring large clustered systems)

0.5.0

Flume (data collection and aggregation)

0.9.4

HBase (real-time read and write database)

0.94.0

HCatalog (table and storage management)

0.4.0

Hive (data summarization and querying)

0.9.0

Lucene (text search)

3.3.0

Oozie (work flow and job orchestration)

3.2.0

Pig (programming and query language)

0.10.1

Sqoop (data transfer between Hadoop and databases)

1.4.1

ZooKeeper (process coordination)

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