Terms in Tech Industry

Tech-related terms that I learned as a (new grad) software engineer:

1. On-Premise
   Refers to software, infrastructure, or IT systems that are deployed and maintained within an organization’s own data center or physical location, rather than being hosted in the cloud.

2. Hadoop
   Apache Hadoop is an open-source, Java-based platform for big data analytics. It manages data processing and storage, enabling simultaneous real-time processing of large volumes of data.

3. SaaS (Software as a Service)
   A cloud-based model for delivering software applications over the internet. Subscription-based and on-demand.

4. CRUD
   Create, Read, Update, Delete—basic operations for persistent storage.

5. OTS (Off-the-Shelf)
   Commercially available hardware or software, ready for immediate use.

6. Kafka
   A distributed streaming platform for handling real-time data feeds. Used for data integration, event-driven systems, and processing pipelines.

7. Service Degradation
   A drop in service performance without total failure.

8. Regression
   A feature that stops working due to recent code changes.

9. Latency Spike
   A sudden increase in response time.

10. Throttling
    Intentional slowing of a service due to resource limits.

11. Docker, Inc.
    Technology to package applications into containers (e.g., Databricks Runtime images).

12. Remote Desktop Protocol (RDP)
    Microsoft protocol allowing remote control of another computer over a network.

13. Data Clean Room
    a. Secure, privacy-centric environment for data collaboration
    b. Used in marketing, analytics, and secure data sharing
    c. Features: data encryption, anonymization, access control
    d. Enables analysis without exposing raw data

14. General Data Protection Regulation (GDPR)
    EU law for data privacy and protection across EU and EEA.

15. Amazon Kinesis
    AWS service for collecting, processing, and analyzing real-time streaming data.

16. VPC (Virtual Private Cloud)
    Isolated virtual network within the public cloud. AWS VPC offers on-premises-like control.

17. OpenAPI Specification (OAS)
    a. Defines consistent REST API interfaces
    b. Written in YAML or JSON
    c. Standard for describing HTTP-based APIs, enabling machine- and human-readability

18. TLS (Transport Layer Security)
    Protocol for securing data over networks via encryption and integrity checks.

19. DBT (data build tool)
    Transforms raw data in a warehouse into clean, analysis-ready datasets.

20. ERP (Enterprise Resource Planning) Integrates core business processes into one system (finance, HR, supply chain, etc.).

21. CIM (Confidential Information Memorandum)
    Document used in M&A to present a company to potential buyers or investors.

22. CIM (Central Invoice Management)
    A unified, centralized approach to receiving, processing, and managing supplier invoices across an organization, typically using a digital platform.

23. NUMA (Non-Uniform Memory Access)
    a. Each processor (or group of processors) has its own local memory
    b. Access to local memory is faster than access to remote memory

24. Prometheus
    a. an open-source monitoring and alerting system designed for reliability and scalability, especially in dynamic cloud-native environments like Kubernetes
    b. Time-series database: Stores metrics as time-series data (value + timestamp)
    c. Pull-based model: Scrapes metrics from targets (e.g., apps, services) via HTTP
    d. PromQL: Powerful query language for analysis and alerting
    e. Service discovery: Automatically finds targets via Kubernetes, Consul, etc.
    f. Alertmanager: Sends alerts via email, Slack, PagerDuty, etc.

25. PromQL(Prometheus Query Language)
    a. used to query time-series data stored in Prometheus
    b. retrieve raw or aggregated metrics; define alerts; build dashbords (e.g. in Grafana)
    c. Metrics | Labels | Time ranges

26. Grafana
    an open-source data visualization and dashboarding tool commonly used with time-series databases like Prometheus, InfluxDB, and Loki

27. OLTP, OLAP, Offline Database

    • OLTP (Online Transaction Processing)
      • Use: Real-time, frequent read/write (e.g., banking, e-commerce)
      • Data: Highly normalized (split into multiple tables to avoid redundancy), up-to-date
      • Operations: INSERT, UPDATE, DELETE
      • Example: MySQL, PostgreSQL
    • OLAP (Online Analytical Processing)
      • Use: Complex queries, analytics, reports
      • Data: Historical, denormalized (data combined in one table)
      • Operations: SELECT with aggregations, GROUP BY
      • Example: Snowflake, BigQuery, Apache Druid
    • Offline Database
      • Use: Batch processing, not time-sensitive
      • Data: Often ingested from OLTP and stored for OLAP or ML
      • Operations: ETL jobs, large-scale joins/aggregations
      • Example: Hadoop HDFS, S3 + Presto