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Deepak Sood's Fundamental Analysis and Technology Notes

website·Indexed 30 days ago

https://deepaksood619.github.io/

A collection of fundamental analysis reports for 2026 covering global stocks and indices including TSMC, ASML, Netflix, Samsung Electronics, and KOSPI, alongside technical documentation on Elasticsearch and Java API.

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What's inside Deepak Sood's Analysis

  1. Overview of AWS Container Services

    AWS provides several specialized services for container management:

    • Amazon Elastic Container Service (ECS): Used to run and manage Docker containers.
    • Amazon Elastic Container Service for Kubernetes (EKS): Used to run managed Kubernetes on AWS.
    • Amazon Elastic Container Registry (ECR): Used to store and retrieve Docker images.

  2. Overview of Firebolt Cloud Data Warehouse

    Firebolt is a cloud data warehouse built for AWS featuring a serverless architecture. It connects to Amazon S3 data lakes as its primary data source and integrates with the broader data ecosystem using standard SQL. For users requiring a self-hosted version, Firebolt Core is available as a free, distributed query engine that can be deployed on a single laptop or within enterprise datacenters.
    # Firebolt Core (Self-hosted edition) is available on GitHub:
# https://github.com/firebolt-db/firebolt-core

  4. Overview of ALEKS Adaptive Learning Platform

    ALEKS (Assessment and LEarning in Knowledge Spaces) is an adaptive learning platform owned by McGraw-Hill Education. It is primarily used for STEM subjects including Mathematics (K-12 through college), Chemistry, Statistics, Accounting, and Business Math. The platform is designed to provide individualized assessment and learning by identifying a student's precise knowledge state to deliver the exact content they need.

  7. Overview of Knative for Kubernetes serverless

    Knative is a Kubernetes-based platform used to build, deploy, and manage modern serverless workloads. It was developed primarily by Google and Pivotal, with contributions from Red Hat and IBM. The platform consists of three main components: Building, Serving, and Eventing. It is dependent on Istio and is available as a managed service via Google Cloud Run.

  9. Overview of Apache Druid capabilities

    Apache Druid is a real-time analytics database designed for fast slice-and-dice OLAP queries on large event-oriented data sets. It is optimized for sub-second queries, high concurrency, and high uptime, making it ideal for powering analytical GUIs and high-concurrency APIs. Key architectural features include:

    • Column-oriented storage for efficient scanning.
    • Native search indexes using CONCISE or Roaring compressed bitmap indexes for fast filtering.
    • Scalable distributed system with massively parallel processing (MPP).
    • Self-healing and self-balancing: adding or removing servers triggers automatic background rebalancing without downtime.
    • Fault-tolerant architecture: data is stored in 'deep storage' (cloud storage, HDFS, or shared filesystem), allowing full recovery even if all Druid servers fail.
    • Support for both streaming (Kafka, Kinesis, Tranquility) and batch ingestion.

  10. Overview of Apache HBase

    Apache HBase is an open-source, distributed, versioned, non-relational database modeled after Google's Bigtable. It is written in Java and runs on top of the Hadoop Distributed File System (HDFS). It is designed for hosting very large tables (billions of rows by millions of columns) on commodity hardware, providing fault-tolerant storage for large quantities of sparse data. In terms of the CAP theorem, HBase is a CP (Consistency and Partition Tolerance) system.

  11. Overview of Stable Diffusion text-to-image model

    Stable Diffusion is a deep learning, text-to-image model released in 2022 by Stability AI. It generates detailed images based on text descriptions and supports additional tasks including inpainting, outpainting, and image-to-image translations guided by text prompts.

  12. Overview of Skip List data structure

    A skip list is a probabilistic data structure that enables fast search within an ordered sequence of elements. It achieves efficiency by maintaining a linked hierarchy of subsequences. Each successive level in the hierarchy skips over fewer elements than the level above it.

    Search Process:

    1. Searching begins at the sparsest (top) subsequence.
    2. The search continues until two consecutive elements are found: one smaller and one larger than or equal to the target element.
    3. The search then drops down to the next level of the hierarchy using the linked pointers and repeats the process.
    4. This continues until the search reaches the full sequence (the bottom level).

    Elements to be skipped are typically chosen probabilistically, though deterministic methods also exist.