Google Protocol Buffer - Serializing Structured Data

**What are Protocol Buffers? **

Protocol buffers language-neutral, platform-neutral extensible mechanism for serializing structured data over the network, think XML but faster, smaller and more straightforward. Google develops it for its internal server to server communication. You define how your data will be structured, and all data structures definition will be saved in the .proto file. Read and write structured data with a variety of data streams and a variety of languages. You can modernize your schemas without making changes to deployed programs that are compiled against the traditional schema. A quick summary before we started with the details –

  • Developed by google
  • Cross-platform software
  • Mostly used for internal protocols

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**Why we need Protobuf? **

JSON has many benefits as an information exchange format- it is human intelligible, understandable and typically performs great. It also has its issues. Where browsers and java scripts are not using the data directly – particularly in the case of internal communication services protocol buffers are the best choice over JSON for encoding data. It is a binary encoding format that permits you to define your schema for your data with a stipulation language. The Protocol Buffers stipulation is performed in different languages: Java, C, Go, etc. are all supported, and most modern languages have a practical implementation.

  • .proto file
  • Python library
  • C++ library
  • Java library

Also, Read The Ultimate Guide to Apache Pulsar – The Stream Processing Platform

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Google Protocol Buffer - Serializing Structured Data
Siphiwe  Nair

Siphiwe Nair


Your Data Architecture: Simple Best Practices for Your Data Strategy

If you accumulate data on which you base your decision-making as an organization, you should probably think about your data architecture and possible best practices.

If you accumulate data on which you base your decision-making as an organization, you most probably need to think about your data architecture and consider possible best practices. Gaining a competitive edge, remaining customer-centric to the greatest extent possible, and streamlining processes to get on-the-button outcomes can all be traced back to an organization’s capacity to build a future-ready data architecture.

In what follows, we offer a short overview of the overarching capabilities of data architecture. These include user-centricity, elasticity, robustness, and the capacity to ensure the seamless flow of data at all times. Added to these are automation enablement, plus security and data governance considerations. These points from our checklist for what we perceive to be an anticipatory analytics ecosystem.

#big data #data science #big data analytics #data analysis #data architecture #data transformation #data platform #data strategy #cloud data platform #data acquisition

Gerhard  Brink

Gerhard Brink


Getting Started With Data Lakes

Frameworks for Efficient Enterprise Analytics

The opportunities big data offers also come with very real challenges that many organizations are facing today. Often, it’s finding the most cost-effective, scalable way to store and process boundless volumes of data in multiple formats that come from a growing number of sources. Then organizations need the analytical capabilities and flexibility to turn this data into insights that can meet their specific business objectives.

This Refcard dives into how a data lake helps tackle these challenges at both ends — from its enhanced architecture that’s designed for efficient data ingestion, storage, and management to its advanced analytics functionality and performance flexibility. You’ll also explore key benefits and common use cases.


As technology continues to evolve with new data sources, such as IoT sensors and social media churning out large volumes of data, there has never been a better time to discuss the possibilities and challenges of managing such data for varying analytical insights. In this Refcard, we dig deep into how data lakes solve the problem of storing and processing enormous amounts of data. While doing so, we also explore the benefits of data lakes, their use cases, and how they differ from data warehouses (DWHs).

This is a preview of the Getting Started With Data Lakes Refcard. To read the entire Refcard, please download the PDF from the link above.

#big data #data analytics #data analysis #business analytics #data warehouse #data storage #data lake #data lake architecture #data lake governance #data lake management

Sid  Schuppe

Sid Schuppe


How To Blend Data in Google Data Studio For Better Data Analysis

Using data to inform decisions is essential to product management, or anything really. And thankfully, we aren’t short of it. Any online application generates an abundance of data and it’s up to us to collect it and then make sense of it.

Google Data Studio helps us understand the meaning behind data, enabling us to build beautiful visualizations and dashboards that transform data into stories. If it wasn’t already, data literacy is as much a fundamental skill as learning to read or write. Or it certainly will be.

Nothing is more powerful than data democracy, where anyone in your organization can regularly make decisions informed with data. As part of enabling this, we need to be able to visualize data in a way that brings it to life and makes it more accessible. I’ve recently been learning how to do this and wanted to share some of the cool ways you can do this in Google Data Studio.

#google-data-studio #blending-data #dashboard #data-visualization #creating-visualizations #how-to-visualize-data #data-analysis #data-visualisation

Cyrus  Kreiger

Cyrus Kreiger


4 Tips To Become A Successful Entry-Level Data Analyst

Companies across every industry rely on big data to make strategic decisions about their business, which is why data analyst roles are constantly in demand. Even as we transition to more automated data collection systems, data analysts remain a crucial piece in the data puzzle. Not only do they build the systems that extract and organize data, but they also make sense of it –– identifying patterns, trends, and formulating actionable insights.

If you think that an entry-level data analyst role might be right for you, you might be wondering what to focus on in the first 90 days on the job. What skills should you have going in and what should you focus on developing in order to advance in this career path?

Let’s take a look at the most important things you need to know.

#data #data-analytics #data-science #data-analysis #big-data-analytics #data-privacy #data-structures #good-company

Sival Alethea

Sival Alethea


Data Structures Easy to Advanced Course - Full Tutorial from a Google Engineer

Learn and master the most common data structures in this full course from Google engineer William Fiset. This course teaches data structures to beginners using high quality animations to represent the data structures visually.

You will learn how to code various data structures together with simple to follow step-by-step instructions. Every data structure presented will be accompanied by some working source code (in Java) to solidify your understanding.
⭐️ Course Contents ⭐️
⌨️ (0:00:00) Abstract data types
⌨️ (0:04:28) Introduction to Big-O
⌨️ (0:17:00) Dynamic and Static Arrays
⌨️ (0:27:40) Dynamic Array Code
⌨️ (0:35:03) Linked Lists Introduction
⌨️ (0:49:16) Doubly Linked List Code
⌨️ (0:58:26) Stack Introduction
⌨️ (1:09:40) Stack Implementation
⌨️ (1:12:49) Stack Code
⌨️ (1:15:58) Queue Introduction
⌨️ (1:22:03) Queue Implementation
⌨️ (1:27:26) Queue Code
⌨️ (1:31:32) Priority Queue Introduction
⌨️ (1:44:16) Priority Queue Min Heaps and Max Heaps
⌨️ (1:49:55) Priority Queue Inserting Elements
⌨️ (1:59:27) Priority Queue Removing Elements
⌨️ (2:13:00) Priority Queue Code
⌨️ (2:28:26) Union Find Introduction
⌨️ (2:33:57) Union Find Kruskal’s Algorithm
⌨️ (2:40:04) Union Find - Union and Find Operations
⌨️ (2:50:30) Union Find Path Compression
⌨️ (2:56:37) Union Find Code
⌨️ (3:03:54) Binary Search Tree Introduction
⌨️ (3:15:57) Binary Search Tree Insertion
⌨️ (3:21:20) Binary Search Tree Removal
⌨️ (3:34:47) Binary Search Tree Traversals
⌨️ (3:46:17) Binary Search Tree Code
⌨️ (3:59:26) Hash table hash function
⌨️ (4:16:25) Hash table separate chaining
⌨️ (4:24:10) Hash table separate chaining source code
⌨️ (4:35:44) Hash table open addressing
⌨️ (4:46:36) Hash table linear probing
⌨️ (5:00:21) Hash table quadratic probing
⌨️ (5:09:32) Hash table double hashing
⌨️ (5:23:56) Hash table open addressing removing
⌨️ (5:31:02) Hash table open addressing code
⌨️ (5:45:36) Fenwick Tree range queries
⌨️ (5:58:46) Fenwick Tree point updates
⌨️ (6:03:09) Fenwick Tree construction
⌨️ (6:09:21) Fenwick tree source code
⌨️ (6:14:47) Suffix Array introduction
⌨️ (6:17:54) Longest Common Prefix (LCP) array
⌨️ (6:21:07) Suffix array finding unique substrings
⌨️ (6:25:36) Longest common substring problem suffix array
⌨️ (6:37:04) Longest common substring problem suffix array part 2
⌨️ (6:43:41) Longest Repeated Substring suffix array
⌨️ (6:48:13) Balanced binary search tree rotations
⌨️ (6:56:43) AVL tree insertion
⌨️ (7:05:42) AVL tree removals
⌨️ (7:14:12) AVL tree source code
⌨️ (7:30:49) Indexed Priority Queue | Data Structure
⌨️ (7:55:10) Indexed Priority Queue | Data Structure | Source Code

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#data structures #data structures easy to advanced course #google engineer #william fiset #data structures easy to advanced course - full tutorial from a google engineer #advanced course