Nigel  Uys

Nigel Uys


Usage of the Heap Data Structure in Go (Golang) with Examples Tutorial

So, Tell Me More About This Heap Data Structure

Heap is one of the most powerful data structures that is in our disposal to solve various real world problems more efficiently. Heap data structure usually comes with two shapes: Min Heap or Max Heap, and depending on which one it is, heap will give you efficient (i.e. O(1)) access to min/max value within the given collection.

Here is the characteristics of the heap data structure, which separate it from other data structure when all of these are combined together:

  • a tree-based data structure, which is a complete binary tree
  • In case of max heap, root node of the tree must represent the greatest value within the tree
  • In case of min heap, root node of the tree must represent the smallest value within the tree
  • Building a heap over an array of values has the cost of O(n log n) in terms of time complexity (worst case), where n is the length of the original array
  • Adding/removing a value from an existing heap has the cost of O(log n) in terms of time complexity, where n is the length of the heap


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Usage of the Heap Data Structure in Go (Golang) with Examples Tutorial
 iOS App Dev

iOS App Dev


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

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

Fannie  Zemlak

Fannie Zemlak


What's new in the go 1.15

Go announced Go 1.15 version on 11 Aug 2020. Highlighted updates and features include Substantial improvements to the Go linker, Improved allocation for small objects at high core counts, X.509 CommonName deprecation, GOPROXY supports skipping proxies that return errors, New embedded tzdata package, Several Core Library improvements and more.

As Go promise for maintaining backward compatibility. After upgrading to the latest Go 1.15 version, almost all existing Golang applications or programs continue to compile and run as older Golang version.

#go #golang #go 1.15 #go features #go improvement #go package #go new features

Jolie  Reichert

Jolie Reichert


How to Use Azure Go SDK to Manage Azure Data Explorer Clusters

Getting started with Azure Data Explorer using the Go SDK covered how to use the Azure Data Explorer Go SDK to ingest and query data from azure data explorer to ingest and query data. In this blog you will the Azure Go SDK to manage Azure Data Explorer clusters and databases.

Azure Data Explorer (also known as Kusto) is a fast and scalable data exploration service for analyzing large volumes of diverse data from any data source, such as websites, applications, IoT devices, and more. This data can then be used for diagnostics, monitoring, reporting, machine learning, and additional analytics capabilities.

In case you’re wondering, we are talking about two different SDKs here. The one covered in this blog is for resource administration (also known as the control plane SDK) and the the one I used in the other post is data plane SDK for interacting with the Azure Data Explorer service itself (ingestion, query etc.)

What’s Covered?

A simple CLI application is used as an example to demonstrate how to use the Go SDK. We’ll try out the application first and go through how to:

  • Create and list Azure Data Explorer clusters
  • Create and list databases in that cluster
  • Delete the database and cluster

Once that’s done, we’ll walk through the sample code to understand what’s going on

The code is available on GitHub

Please note that this CLI based example is just meant to showcase how to use the Azure Go SDK (in the context of Azure Data Explorer) as a part of a larger application. It is not supposed to replace/substitute the Azure CLI which can be used to manage Azure Data Explorer resources


Install Go 1.13 or above

You will need a Microsoft Azure account. Go ahead and sign up for a free one!

Install the Azure CLI if you don’t have it already (should be quick!)

#tutorial #big data #azure #analytics #go #golang #azure data explorer clusters #azure go sdk