A Start-Up's Guide to Data Analytics (Part One)

Part One — Building A Data Warehouse

Nowadays, everyone wants to build a data warehouse. But does one really need it? Even if you need it, how do you know you’re building the right thing and when are you really going to start to reaping early benefits from it?

But first things first, what is a data warehouse? Simply put, it’s a single place where you can store data from all sources. It helps one answer the questions that require complex analysis involving data from multiple sources. You can also build a data warehouse in a fashion that you get your most frequent data requirements taken care of quickly.

A year ago, we were struggling with this question at UpGrad — to build or not to build a data warehouse?

In order to answer this, and many other such questions, we talked to a lot of other people who had done it before. The first thing that we noticed was that to build a data warehouse (or DW), you need the right team of data engineers, architects, analysts and product managers. The first question we asked was — is it really worth that much investment?

To find the right answer, we need to ask ourselves the right set of questions. These questions might take a good deal of time and energy, but once you are done with these, you will be far more confident about whether to move ahead with DW or not. Here, we’ll provide the answers we got from our own exercise to enhance your understanding, and hopefully aid you in this process of deciding whether or not to set up your own data warehouse.

Question #1: What answers do you want to get from analytics/data? And at what frequency?

As you must have noted already, this is the most important question of all. You must involve other teams (Sales, Marketing, Business) while answering these questions to make sure you don’t miss anything.

#data science #data #data analytics #data scientists

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A Start-Up's Guide to Data Analytics (Part One)
Gerhard  Brink

Gerhard Brink

1620629020

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.

Introduction

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

Siphiwe  Nair

Siphiwe Nair

1620466520

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

Siphiwe  Nair

Siphiwe Nair

1625133780

SingleStore: The One Stop Shop For Everything Data

  • SingleStore works toward helping businesses embrace digital innovation by operationalising “all data through one platform for all the moments that matter”

The pandemic has brought a period of transformation across businesses globally, pushing data and analytics to the forefront of decision making. Starting from enabling advanced data-driven operations to creating intelligent workflows, enterprise leaders have been looking to transform every part of their organisation.

SingleStore is one of the leading companies in the world, offering a unified database to facilitate fast analytics for organisations looking to embrace diverse data and accelerate their innovations. It provides an SQL platform to help companies aggregate, manage, and use the vast trove of data distributed across silos in multiple clouds and on-premise environments.

**Your expertise needed! **Fill up our quick Survey

#featured #data analytics #data warehouse augmentation #database #database management #fast analytics #memsql #modern database #modernising data platforms #one stop shop for data #singlestore #singlestore data analytics #singlestore database #singlestore one stop shop for data #singlestore unified database #sql #sql database

Ian  Robinson

Ian Robinson

1624399200

Top 10 Big Data Tools for Data Management and Analytics

Introduction to Big Data

What exactly is Big Data? Big Data is nothing but large and complex data sets, which can be both structured and unstructured. Its concept encompasses the infrastructures, technologies, and Big Data Tools created to manage this large amount of information.

To fulfill the need to achieve high-performance, Big Data Analytics tools play a vital role. Further, various Big Data tools and frameworks are responsible for retrieving meaningful information from a huge set of data.

List of Big Data Tools & Frameworks

The most important as well as popular Big Data Analytics Open Source Tools which are used in 2020 are as follows:

  1. Big Data Framework
  2. Data Storage Tools
  3. Data Visualization Tools
  4. Big Data Processing Tools
  5. Data Preprocessing Tools
  6. Data Wrangling Tools
  7. Big Data Testing Tools
  8. Data Governance Tools
  9. Security Management Tools
  10. Real-Time Data Streaming Tools

#big data engineering #top 10 big data tools for data management and analytics #big data tools for data management and analytics #tools for data management #analytics #top big data tools for data management and analytics

A Start-Up's Guide to Data Analytics (Part One)

Part One — Building A Data Warehouse

Nowadays, everyone wants to build a data warehouse. But does one really need it? Even if you need it, how do you know you’re building the right thing and when are you really going to start to reaping early benefits from it?

But first things first, what is a data warehouse? Simply put, it’s a single place where you can store data from all sources. It helps one answer the questions that require complex analysis involving data from multiple sources. You can also build a data warehouse in a fashion that you get your most frequent data requirements taken care of quickly.

A year ago, we were struggling with this question at UpGrad — to build or not to build a data warehouse?

In order to answer this, and many other such questions, we talked to a lot of other people who had done it before. The first thing that we noticed was that to build a data warehouse (or DW), you need the right team of data engineers, architects, analysts and product managers. The first question we asked was — is it really worth that much investment?

To find the right answer, we need to ask ourselves the right set of questions. These questions might take a good deal of time and energy, but once you are done with these, you will be far more confident about whether to move ahead with DW or not. Here, we’ll provide the answers we got from our own exercise to enhance your understanding, and hopefully aid you in this process of deciding whether or not to set up your own data warehouse.

Question #1: What answers do you want to get from analytics/data? And at what frequency?

As you must have noted already, this is the most important question of all. You must involve other teams (Sales, Marketing, Business) while answering these questions to make sure you don’t miss anything.

#data science #data #data analytics #data scientists