A Sample Road-map for Building Your Data Warehouse

Data warehousing, a technique of consolidating all of your organisational data into one place for easier access and better analytics, is every business stakeholder’s dream. However, setting up a data warehouse is a significantly complex task, and even before taking your first steps, you should be utterly sure about the answer to these two questions:

Your organisation’s goals
Your detailed roadmap to building a data warehouse

Either of these questions, if left unanswered, can cost your organisation a lot in the long run. It’s a relatively newer technology, and you’re going to create a lot of scope for errors if you’re not aware of your organisation’s specific needs and requirements. These errors can render your warehouse highly inaccurate. What’s worse is that an erroneous data warehouse is worse than not having data at all and an unplanned strategy might end up doing you more bad than good.

Because there are different approaches to developing data warehouses and each depends on the size and needs of organisations, it’s not possible to create a one-shoe-fits-all plan.

Having said that, let’s try to lay out a sample roadmap that’ll help you develop a robust and efficient data warehouse for your organisation:

Setting up a Data Warehouse

Data Warehouse is extremely helpful when organizing large amounts of data to retrieve and analyse efficiently. For the same reason, extreme care should be taken to ensure that the data is rapidly accessible. One approach to designing the system is by using dimensional modelling – a method that allows large volumes of data to be efficiently and quickly queried and examined. Since most of the data present in data warehouses are historical and stable – in a sense, it doesn’t change frequently, there is hardly a need to employ repetitive backup methods. Instead, once any data is added, the entire warehouse can be backed up at once – instead of backing up routinely.

Data warehousing tools can be broadly classified into four categories:

Extraction tools,
Table management tools,
Query management tools, and
Data integrity tools.

Each of these tools come in extremely handy at different stages of development of the Data Warehouse. Research on your part will help you understand more about these tools, and will allow you to can pick the ones which suit your needs.

Key Concepts of Data Warehousing: An Overview

Now, let’s look at a sample roadmap that’ll help you build a more robust and insightful warehouse for your organisation:

Evaluate your objectives

The first step in setting up your organisation’s data warehouse is to evaluate your goals. We’ve mentioned this earlier, but we can’t stress this enough. Most of the organisations lose out on valuable insights just because they lack a clear picture of their company’s objectives, requirements, and goals. For instance, if you’re a company looking for your first significant breakthrough, you might want to engage your customers in building rapport – so, you’ll need to follow a different approach than an organisation that’s well established and now wants to use the data warehouse for improving their operations. Bringing a data warehouse in-house is a big step for any organisation and should be performed only after some due diligence on your part.

Analyse current technological systems

By asking your customers and business stakeholders pointed questions, you can gather insights on how your current technical system is performing, the challenges it’s facing, and the improvements possible. Further, they can even find out how suitable their current technology stack is – thereby efficiently deciding whether it is to be kept or replaced. Various department of your organisation can contribute to this by providing reports and feedback.

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A Sample Road-map for Building Your Data Warehouse

Sid Schuppe

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Benefits of Hybrid Cloud for Data Warehouse

In today’s market reliable data is worth its weight in gold, and having a single source of truth for business-related queries is a must-have for organizations of all sizes. For decades companies have turned to data warehouses to consolidate operational and transactional information, but many existing data warehouses are no longer able to keep up with the data demands of the current business climate. They are hard to scale, inflexible, and simply incapable of handling the large volumes of data and increasingly complex queries.

These days organizations need a faster, more efficient, and modern data warehouse that is robust enough to handle large amounts of data and multiple users while simultaneously delivering real-time query results. And that is where hybrid cloud comes in. As increasing volumes of data are being generated and stored in the cloud, enterprises are rethinking their strategies for data warehousing and analytics. Hybrid cloud data warehouses allow you to utilize existing resources and architectures while streamlining your data and cloud goals.

#cloud #data analytics #business intelligence #hybrid cloud #data warehouse #data storage #data management solutions #master data management #data warehouse architecture #data warehouses

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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

Rowland Goodwin

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Database Vs Data Warehouse Vs Data Lake: A Simple Explanation

Databases store data in a structured form. The structure makes it possible to find and edit data. With their structured structure, databases are used for data management, data storage, data evaluation, and targeted processing of data.
In this sense, data is all information that is to be saved and later reused in various contexts. These can be date and time values, texts, addresses, numbers, but also pictures. The data should be able to be evaluated and processed later.

The amount of data the database could store is limited, so enterprise companies tend to use data warehouses, which are versions for huge streams of data.

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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.

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