Data Warehouse Business Intelligence Tools – At its core, business intelligence is the ability to answer complex questions about your data and use those answers to make informed business decisions. To do this, you need a data warehouse that not only provides a secure way to organize and store your data, but also allows you to get the answers you need, when you need them, quickly.
And this is a very important role. By 2025, it is estimated that humanity will generate a total of 175 zettabytes of data. For context, that’s 175,000,000,000 terabytes.
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Companies use data warehouses to manage transactions, understand and store their data in an organized manner. In short, data warehouses make large amounts of information more usable for organizations of all sizes and types.
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This has made them the hub of data pipelines and business intelligence systems around the world. And understanding how data warehouses work can help unlock the full potential of business intelligence (it’s not as hard as it seems).
A data warehouse is a data management system that stores large amounts of data for further use in processing and analysis. You can think of it as a big warehouse where trucks (ie source data) load their data. This data is then organized into rows and well-organized rows on shelves, making it easier to find later.
You can store this data in three different ways: on-premises data stores, cloud data stores, and hybrid data stores.
Internal data warehouses run and manage your company’s physical servers. Cloud data stores are completely online and you pay for space on servers managed by another company, such as Amazon Redshift. Combined data storage is a mix of both on-premise and cloud, and companies moving to the cloud have long embraced this option.
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When all the data is stored in one place, databases use a specific data processing method called online analytical processing (OLAP), which is specially designed for complex queries.
Another way to think about it is if you go to your data warehouse to ask a question about the relationship between one set of data and another, OLAP is a way of organizing and moving between rows and rows of shelves to quickly find that information. .
This is good for business intelligence because the questions you ask your data to make decisions are often not easy. Because data warehouses use OLAP, they make finding answers to these difficult questions very efficient. As a result, they have become the basis of many successful business intelligence programs.
From a business perspective, data warehouses act as the backbone of data storage. Business intelligence relies on asking complex questions and comparing multiple sets of data to inform everything from day-to-day decisions to organization-wide shifts in focus.
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To simplify, business intelligence consists of three broad functions: data analysis, data warehousing, and data analysis. Data compression is often simplified with extract, transform, load (ETL) technology, which we describe in detail below, and data analysis is done using business intelligence tools such as
The glue that holds this process together is data warehousing, which acts as a catalyst for data warehousing using OLAP. They collect, summarize and transform data to facilitate analysis.
Although data warehouses serve as the backbone of data storage, they are not the only technology involved in data storage. Many companies go through the data retention process until they reach the point where they absolutely need data retention.
As we explain in our Cloud Data Management eBook (a very easy – and dare we say it – fun read), there are generally four levels of data complexity: source data, data lakes, data warehouses and marts of data. Knowing when to invest in data storage requires knowing each phase, but at the end of the day, the data storage phase is what unlocks the true power of your data.
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Source data is any set of data, such as databases, Excel spreadsheets, single application reports, etc. Structured (ie organized) but compiled data works well on its own, but doesn’t provide a big picture of your organization’s data as a whole.
For teams that are done needing to consolidate their source data in one place, the data lake becomes the next step. A data foundation acts as a central repository for all raw, unstructured (ie random) data.
If a data warehouse is like backing up a truck and systematically moving the data into a well-planned scraping system, data lakes are like backing up a truck and dumping all the data into a pond. James Dixon, who coined the term “data lake,” describes it as a natural raw state of data that people can dive into, serving as a boundary for their research.
A disadvantage of a data lake is that the data is not ready for analysis. It’s not well organized, it can be repetitive, and to understand it, you have to tell your surfer what you want. However, the diver may not get exactly what he needs after all that effort.
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Like a data lake, a data warehouse centralizes your data, but as we discovered, it’s well organized and designed for efficient analysis. It’s a single source of truth for all data that’s easy to understand and navigate.
Data warehouses can be connected to extract data, but today we see many companies using a data warehouse as a layer on top of a data lake. In Dixon’s analogy, if a data lake is water/data in its natural, unstructured state, a data warehouse is where you manage it and prepare it for use.
If you’re in the market for a data warehouse, read our 5 tips for choosing the right data warehouse to get you started on the right track.
Using a data warehouse for specific projects can be like swatting a fly with a hammer. If, for example, the marketing team periodically returns to the warehouse for the same request, you can set up a data mart.
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Datasets are ordered collections of data designed for specific use cases. And, to paraphrase Dixon, the sales team doesn’t need to go to the treatment center every time they need water. The data storage area can be used to package data/water into ready-to-drink “water bottles”.
In this data storage system, data storage is still fundamental. It’s structured and easy to understand (like source data), but provides a comprehensive, centralized view (like a data lake) that makes it very easy to use that data as you need it (for example, creating data tags).
Databases are complex systems, but can be thought of as consisting of three main elements: storage, software, and functionality. When you decide to start a data warehouse, you should consider the investment required for all three.
Saving is an easy choice. As mentioned earlier, you can store your data on-premises, in the cloud, or use a hybrid approach. Local hosting, according to some, is on the way out. Cloud hosting is much cheaper and more flexible because you rent space on someone else’s server. You don’t have to do any maintenance, you can scale up and down as needed, and an ever-increasing feature set is added every year. Bridging the gap between these two approaches is hybrid hosting, which, as mentioned earlier, is a popular choice for companies moving from on-premise to cloud hosting.
To get data into a data warehouse, you need to use a type of software commonly called ETL software. Extract, transform, load (ETL) is a process where data is extracted, ready for use and loaded into a data warehouse.
Today, we advise and see more and more companies using an alternative ETL method called Extract, Load, Transform (ELT). Often companies will extract data from source data, load it into a data lake, and then use data warehouses to transform the data. Both ETL and ELT are facilitated by software like Panoply.io and Stitch. If you want to learn more, check out our detailed resources on ETL, ELT and ETLT.
Of course, data warehouses don’t work by themselves. Work is an important part of database performance because it is not just a program; “A complete structure …”, which requires professionals to create and manage.
The purpose of all this work is to organize and organize the data so that it is easy to understand. This is where business intelligence tools come in. They sit on top of data stores as a layer that helps you query, analyze and visualize your data.
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While data warehouses store data, business intelligence platforms analyze data. When you keep these two systems running smoothly, you get the full benefits of business intelligence.
Business intelligence tools complement the “data analysis” business intelligence category, but they get their name because they are the culmination of two other steps: data compression and data warehousing.
First, business intelligence tools include many different sources, including your data warehouse. Then they provide an easy way to do it
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