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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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5 HBase Vs. RDBMS Top Functional Differences

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Here're the differences between RDBMS and HBase. HBase in the Big data context has a lot of benefits over RDBMS. The listed differences below make it understandable why HBASE is popular in Hadoop (or Bigdata) platform. 5 HBase Vs. RDBMS Top Functional Differences Here're the differences unlock now. Random Accessing HBase handles a large amount of data that is store in a distributed manner in the column-oriented format while RDBMS is systematic storage of a database that cannot support a random manner for accessing the database. Database Rules RDBMS strictly follows Codd's 12 rules with fixed schemas and row-oriented manner of database and also follows ACID properties. HBase follows BASE properties and implements complex queries. Secondary indexes, complex inner and outer joins, count, sum, sort, group, and data of page and table can easily be accessible by RDBMS. Storage From small to medium storage application there is the use of RDBMS that provides the solution with MySQ...
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RDBMS Vs Key-value Four Top Differences

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This post tells you differences between rdbms and distributed key-value storage. Rdbms is quite  different from key-value storage. RDBMS (Relational Database) You have already used a  r elational  d atabase  m anagement  s ystem — a storage product that's commonly referred to as  RDBMS .  It is basically a structured data. RDBMS systems are fantastically useful to handle moderate data. The BIG challenge is in scaling beyond a single server.  You can't maintain redundant data in rdbms. All the data available on single server. The entire database runs on single server. So when server is down then database may not be available to normal business operations. Outages and server downs are common in this rdbms model of database. Key-Value Database Key-value storage systems often make use of redundancy within hardware resources to prevent outages. This concept is important when you're running thousands of servers because they're bound...
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RDBMS Vs NOSQL awesome differences to read now

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NoSQL and RDBMS or SQL are different from each other. You may ask what is the difference. Below explained in a way that you can understand quickly. 💡Traditional Database A schema is required. All traditional data warehouses using RDBMS to store datamarts. Databases understand SQL language. It has a specific format and rules to interact with traditional databases. Less scalable. It has certain limitations.  Expensive to make the databases as scalable Data should be in a certain format. Data stored in row format. NoSQL database The growing internet usage and involving a number of devices caused to invent databases that have the capability to store any kind of data. More: MongoDB 3.2 fundamentals for Developers-Learn with Exercises NoSQL Special Features The schema is not required. Ability to handle multiple data types. This is the power of NoSQL. NoSQL is much suitable for analytical databases. Since those should be flexible, scalable, and able to st...
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