Dataversity

JANUARY 28, 2021

It has been ten years since Pentaho Chief Technology Officer James Dixon coined the term “data lake.” While data warehouse (DWH) systems have had longer existence and recognition, the data industry has embraced the more […]. The post A Bridge Between Data Lakes and Data Warehouses appeared first on DATAVERSITY.

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Simplilearn

JULY 18, 2024

In the era of big data, businesses and organizations continuously seek innovative ways to handle and leverage their vast amounts of data efficiently. This quest for data optimization has led to the emergence and evolution of data lakes and data warehouses, two pivotal structures in the data management landscape.

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Smart Data Collective

OCTOBER 13, 2021

There are countless examples of big data transforming many different industries. There is no disputing the fact that the collection and analysis of massive amounts of unstructured data has been a huge breakthrough. We would like to talk about data visualization and its role in the big data movement.

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Smart Data Collective

JUNE 15, 2021

It’s also possible to employ extra caching or materialized views in the data warehouse in addition to caching in Looker (depending on the capability of your data warehouse). One added tip is to aggregate your data before loading it into Looker or in the data warehouse to reduce the amount of data loaded onto the platform.

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Smart Data Collective

NOVEMBER 18, 2020

The Bureau of Labor Statistics estimates that the number of data scientists will increase from 32,700 to 37,700 between 2019 and 2029. Unfortunately, despite the growing interest in big data careers, many people don’t know how to pursue them properly. Data Mining is an important research process.

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Smart Data Collective

DECEMBER 13, 2022

However, computerization in the digital age creates massive volumes of data, which has resulted in the formation of several industries, all of which rely on data and its ever-increasing relevance. Data analytics and visualization help with many such use cases. It is the time of big data. Select a Storage Platform.

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Simplilearn

DECEMBER 27, 2022

Enterprises utilize data to optimize practically all business operations today. Traditional databases, on the other hand, do not meet the shifting demands of data analysis, which need access to big data for visualization and reporting. However, they do n. Read More.

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Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

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BizAcuity

APRIL 14, 2022

Big Data technology in today’s world. Did you know that the big data and business analytics market is valued at $198.08 Or that the US economy loses up to $3 trillion per year due to poor data quality? quintillion bytes of data which means an average person generates over 1.5 Big Data Ecosystem.

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Smart Data Collective

OCTOBER 17, 2022

While growing data enables companies to set baselines, benchmarks, and targets to keep moving ahead, it poses a question as to what actually causes it and what it means to your organization’s engineering team efficiency. What’s causing the data explosion? Big data analytics from 2022 show a dramatic surge in information consumption.

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Smart Data Collective

SEPTEMBER 26, 2021

Working with massive structured and unstructured data sets can turn out to be complicated. It’s obvious that you’ll want to use big data, but it’s not so obvious how you’re going to work with it. So, let’s have a close look at some of the best strategies to work with large data sets.

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Smart Data Collective

MARCH 30, 2022

Azure Data Lake Storage Gen2 is based on Azure Blob storage and offers a suite of big data analytics features. If you don’t understand the concept, you might want to check out our previous article on the difference between data lakes and data warehouses.

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Dataversity

DECEMBER 14, 2020

It was only a few years ago that BI and data experts excitedly claimed that petabytes of unstructured data could be brought under control with data pipelines and orderly, efficient data warehouses. But as big data continued to grow and the amount of stored information increased every […].

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Smart Data Collective

APRIL 29, 2020

The extraction of raw data, transforming to a suitable format for business needs, and loading into a data warehouse. Data transformation. This process helps to transform raw data into clean data that can be analysed and aggregated. Data analytics and visualisation.

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Smart Data Collective

JULY 9, 2023

ETL is a three-step process that involves extracting data from various sources, transforming it into a consistent format, and loading it into a target database or data warehouse. Extract The extraction phase involves retrieving data from diverse sources such as databases, spreadsheets, APIs, or other systems.

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Smart Data Collective

JUNE 16, 2022

The data is processed and modified after it has been extracted. Data is fed into an Analytical server (or OLAP cube), which calculates information ahead of time for later analysis. A data warehouse extracts data from a variety of sources and formats, including text files, excel sheets, multimedia files, and so on.

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Sisense

SEPTEMBER 3, 2019

This blog is intended to give an overview of the considerations you’ll want to make as you build your Redshift data warehouse to ensure you are getting the optimal performance. This results in less joins between the metric data in fact tables, and the dimensions. So let’s dive in! OLTP vs OLAP.

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Astera

OCTOBER 27, 2023

What is a Cloud Data Warehouse? Simply put, a cloud data warehouse is a data warehouse that exists in the cloud environment, capable of combining exabytes of data from multiple sources. A cloud data warehouse is critical to make quick, data-driven decisions.

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Domo

JULY 10, 2014

The data warehouse seems like a perfect BI solution. It provides a central repository for storing all of an organization’s data that, in turn, gives reporting tools a single location where those tools can find and extract useful data that can then be analyzed and consumed. The disappointing reality. Yes, never.

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Smart Data Collective

APRIL 15, 2021

Set Up Data Integration. Data warehouses, a database that keeps the information in a processed and defined format, cannot connect directly to information sources, so data integration tools must process the raw data ahead of time to allow it to be usable. Laying out these components will be helpful down the line.

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Sisense

MARCH 20, 2019

To do that, a data engineer needs to be skilled in a variety of platforms and languages. In our never-ending quest to make BI better, we took it upon ourselves to list the skills and tools every data engineer needs to tackle the ever-growing pile of Big Data that every company faces today. Data Warehousing.

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Astera

APRIL 13, 2023

In the digital age, a data warehouse plays a crucial role in businesses across several industries. It provides a systematic way to collect and analyze large amounts of data from multiple sources, such as marketing, sales, finance databases, and web analytics. What is a Data Warehouse?

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Smart Data Collective

MARCH 21, 2022

More case studies are added every day and give a clear hint – data analytics are all set to change, again! . Data Management before the ‘Mesh’. In the early days, organizations used a central data warehouse to drive their data analytics. This is also true that decentralized data management is not new.

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Astera

NOVEMBER 14, 2023

If you have had a discussion with a data engineer or architect on building an agile data warehouse design or maintaining a data warehouse architecture, you’d probably hear them say that it is a continuous process and doesn’t really have a definite end. What do you need to build an agile data warehouse?

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Sisense

MARCH 14, 2021

Attempting to learn more about the role of big data (here taken to datasets of high volume, velocity, and variety) within business intelligence today, can sometimes create more confusion than it alleviates, as vital terms are used interchangeably instead of distinctly. Big data challenges and solutions.

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Domo

JULY 15, 2014

The tech world has long talked about relational databases, but in a data warehouse, relating data is tough because the data comes from so many sources. A half-mile per gallon increase, thanks to data. If you do not, you will always have incomplete data and therefore decisions based on incomplete information.

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BizAcuity

AUGUST 1, 2022

Data Security. Good: Self-service capability, ability to work with big data, users can build their own data mart or warehouse. JasperSoft for Big Data Analytics. Reports and dashboards can be generated directly from the data warehouse or data lake. Good Visualization Options.

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Business Analysis Knowledge Share

MARCH 15, 2023

In recent years, there has been a growing interest in NoSQL databases, which are designed to handle large volumes of unstructured or semi-structured data. These databases are often used in big data applications, where traditional relational databases may not be able to handle the scale and complexity of the data.

Big Data 52

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Astera

NOVEMBER 1, 2023

Businesses rely heavily on various technologies to manage and analyze their growing amounts of data. Data warehouses and databases are two key technologies that play a crucial role in data management. While both are meant for storing and retrieving data, they serve different purposes and have distinct characteristics.

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Tableau

JUNE 8, 2021

In many of the conversations we have with IT and business leaders, there is a sense of frustration about the speed of time-to-value for big data and data science projects. We often hear that organizations have invested in data science capabilities but are struggling to operationalize their machine learning models.

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Actian

JANUARY 14, 2020

There are a lot of myths and misconceptions about cloud data warehouses. One of the biggest ones is that all cloud data warehouses cost the same. On the surface, cloud data warehouse vendors may talk the same language – describing similar features, benefits and touting the performance gains of operating in the cloud.

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ElegantJ BI

JUNE 17, 2015

Since I couldn’t be in two places at the same time, I tried to make the choices that were most relevant to our team, our customers and our partners, and I chose the following sessions.

Business Intelligence 130

Business Intelligence Business Intelligence Data Warehouse Data Discovery 130

ElegantJ BI

JUNE 17, 2015

Since I couldn’t be in two places at the same time, I tried to make the choices that were most relevant to our team, our customers and our partners, and I chose the following sessions.

Business Intelligence 130

Business Intelligence Business Intelligence Data Warehouse Data Discovery 130

ElegantJ BI

JUNE 17, 2015

Do We Still Need a Data Warehouse – Roxanne Edijali. Navigating the Data Lake – Adam Ronthal. Big Data Discovery – Rita Sallam. Interactive Visualizations for Everyone – Rita Sallam. Mobile BI – It’s Time to Innovate – Bhavish Sood.

Business Intelligence 130

Business Intelligence Business Intelligence Data Warehouse Data Discovery 130

Data Pine

OCTOBER 25, 2022

With ‘big data’ transcending one of the biggest business intelligence buzzwords of recent years to a living, breathing driver of sustainable success in a competitive digital age, it might be time to jump on the statistical bandwagon, so to speak. of all data is currently analyzed and used. click for book source**.

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Sisense

JANUARY 1, 2020

5 Advantages of Using a Redshift Data Warehouse. Whatever business you’re in, your company is becoming a data company. That means you need to put all that data somewhere. Chances are it’s in a data warehouse, and even better money says it’s an AWS data warehouse. Read about how Sisense BloX 2.0

Digital Transformation 98

Digital Transformation Data Warehouse Embedded Analytics Artificial Intelligence 98

Smart Data Collective

JULY 22, 2019

Automating your data processing routine can offer your business a lot of benefits. BI tools use the Big Data approach and apply it to your company data. Looker is a data-discovery BI tool that helps companies of different scales find the best business solutions thanks to real-time data access.

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