Astera

FEBRUARY 14, 2024

Data vault is an emerging technology that enables transparent, agile, and flexible data architectures, making data-driven organizations always ready for evolving business needs. What is a Data Vault?  A data vault is a data modeling technique that enables you to build data warehouses for enterprise-scale analytics.

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Data Pine

MARCH 31, 2022

4) Business Intelligence Job Roles. Does data excite, inspire, or even amaze you? Do you find computer science and its applications within the business world more than interesting? If you answered yes to any of these questions, you may want to consider a career in business intelligence (BI).In

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Astera

JANUARY 25, 2024

Reverse ETL (Extract, Transform, Load) is the process of moving data from central data warehouse to operational and analytic tools. How Does Reverse ETL Fit in Your Data Infrastructure Reverse ETL helps bridge the gap between central data warehouse and operational applications and systems.

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Astera

JANUARY 23, 2024

Businesses need scalable, agile, and accurate data to derive business intelligence (BI) and make informed decisions. Their data architecture should be able to handle growing data volumes and user demands, deliver insights swiftly and iteratively. What are Information Marts?

Agile 52

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Astera

APRIL 27, 2023

This is where data extraction tools from companies like Matillion, Astera , and Fivetran are used to organize and prepare data for a cloud data warehouse. ELT or ETL tools , such as DBT, work within a cloud data warehouse to convert, clean, and structure data, into a format usable by data engineers and analysts.

Data Warehouse 52

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Astera

DECEMBER 14, 2022

Kimball-style dimensional modeling has been the go-to architecture for most data warehouse developers over the past couple of decades. The questions that arise, however, are the following: How easy is it to load and maintain data in fact and dimension tables? And Is it worth the effort?

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Astera

JUNE 14, 2023

These transactions typically involve inserting, updating, or deleting small amounts of data. Normalized data structure: OLTP databases have a normalized data structure. This means that they use a data model that minimizes redundancy and ensures data consistency. through a built-in OData service.

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The Data Administration Newsletter

APRIL 19, 2022

Introduction We are living in the age of a data revolution, and more corporations are realizing that to lead—or in some cases, to survive—they need to harness their data wealth effectively.

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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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Astera

DECEMBER 22, 2022

Enterprises will soon be responsible for creating and managing 60% of the global data. Traditional data warehouse architectures struggle to keep up with the ever-evolving data requirements, so enterprises are adopting a more sustainable approach to data warehousing. Best Practices to Build Your 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. displaying BI insights for human users).

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Astera

DECEMBER 29, 2023

Among the key players in this domain is Microsoft, with its extensive line of products and services, including SQL Server data warehouse. In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way.

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Astera

DECEMBER 29, 2023

Among the key players in this domain is Microsoft, with its extensive line of products and services, including SQL Server data warehouse. In this article, we’re going to talk about Microsoft’s SQL Server-based data warehouse in detail, but first, let’s quickly get the basics out of the way.

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Actian

JUNE 17, 2019

For companies that want to develop a sustainable competitive advantage, they must start aggregating, organizing and refining their massive data stores into the real business intelligence that leads to better decisions and more efficient operations. “We We are struggling to convert data into actionable insights.”.

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Astera

APRIL 7, 2023

Finally, the stored data is retrieved at optimal speeds to support efficient analysis and decision-making. Essentially, a data warehouse also acts as a centralized database for storing structured, analysis-ready data and giving a holistic view of this data to decision-makers.

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Astera

JUNE 8, 2023

Data Integrity and Concurrency Control: Oracle ensures data integrity through constraints, triggers, and advanced concurrency control techniques. Data Analytics and Business Intelligence: Oracle supports powerful data analytics and business intelligence, enabling robust analysis, reporting, and decision-making.

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Astera

JUNE 8, 2023

Data Integrity and Concurrency Control: Oracle ensures data integrity through constraints, triggers, and advanced concurrency control techniques. Data Analytics and Business Intelligence: Oracle supports powerful data analytics and business intelligence, enabling robust analysis, reporting, and decision-making.

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Sisense

MAY 31, 2020

We’ve taken what we’ve learned from our customers and combined it with our own understanding of how the data and analytics world is evolving to drive innovations that unlock new possibilities and help our clients future-proof their products and services. ” Chris Wallingford, Director of Business Intelligence, Tessitura Network.

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Smart Data Collective

JULY 9, 2023

ETL (Extract, Transform, Load) is a crucial process in the world of data analytics and business intelligence. In this article, we will explore the significance of ETL and how it plays a vital role in enabling effective decision making within businesses. What is ETL? Let’s break down each step: 1.

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Sisense

FEBRUARY 16, 2021

Every aspect of analytics is powered by a data model. A data model presents a “single source of truth” that all analytics queries are based on, from internal reports and insights embedded into applications to the data underlying AI algorithms and much more. Data modeling organizes and transforms data.

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Sisense

MAY 11, 2021

You can’t talk about data analytics without talking about data modeling. The reasons for this are simple: Before you can start analyzing data, huge datasets like data lakes must be modeled or transformed to be usable. Building the right data model is an important part of your data strategy.

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Astera

MARCH 7, 2024

ETL testing is a set of procedures used to evaluate and validate the data integration process in a data warehouse environment. In other words, it’s a way to verify that the data from your source systems is extracted, transformed, and loaded into the target storage as required by your business rules.

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Smart Data Collective

JUNE 16, 2022

Online analytical processing is a computer method that enables users to retrieve and query data rapidly and carefully in order to study it from a variety of angles. Trend analysis, financial reporting, and sales forecasting are frequently aided by OLAP business intelligence queries. ( see more ).

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Astera

MAY 25, 2023

Unlocking the Potential of Amazon Redshift Amazon Redshift is a powerful cloud-based data warehouse that enables quick and efficient processing and analysis of big data. Amazon Redshift can handle large volumes of data without sacrificing performance or scalability. What Is Amazon Redshift? 

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Data Pine

SEPTEMBER 29, 2022

With quality data at their disposal, organizations can form data warehouses for the purposes of examining trends and establishing future-facing strategies. Industry-wide, the positive ROI on quality data is well understood. The program manager should lead the vision for quality data and ROI. 2 – Data profiling.

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Astera

JANUARY 3, 2023

Dimensional modeling is among the most preferred design approaches for building analytics-friendly data warehouses. First introduced in 1996, Kimball ’ s dimension al data models have now bec o me cornerstones of modern data warehouse design and development. Dimensional Data Model.

Data Warehouse 52

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Astera

SEPTEMBER 1, 2023

The significance of data warehousing for insurance cannot be overstated. It forms the bedrock of modern insurance operations, facilitating data-driven insights and streamlined processes to better serve policyholders. The data warehouse has the highest adoption of data solutions, used by 54% of organizations.

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Astera

NOVEMBER 28, 2022

Data warehouses have long served as a single source of truth for data-driven companies. But as data complexity and volumes increase, it’s time to look beyond the traditional data ecosystems. Does that mean it’s the end of data warehousing? Does that mean it’s the end of data warehousing?

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Sisense

JANUARY 15, 2021

Access to information can be a game-changer for businesses looking to unlock strategic advantages through analytical insights. Ensuring that your organization has the right business intelligence and analytics tools to drive this innovation is key. It’s no secret that data teams are becoming indispensable to organizations.

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ElegantJ BI

JUNE 1, 2022

An integrated solution provides single sign-on access to data sources and data warehouses.’ You can create common data models and BI object templates to publish across tenants with a single click. Integrating augmented analytics within your existing software solutions is simple.

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ElegantJ BI

JUNE 1, 2022

An integrated solution provides single sign-on access to data sources and data warehouses.’ You can create common data models and BI object templates to publish across tenants with a single click. Integrating augmented analytics within your existing software solutions is simple.

Data Warehouse 130

Data Warehouse Data Modelling Data Architecture Embedded Analytics 130

ElegantJ BI

JUNE 1, 2022

An integrated solution provides single sign-on access to data sources and data warehouses.’. The integrated augmented analytics approach includes simple tenant management to deploy with a shared data model for single-tenant mode or an isolated data model for multi-tenant mode and software as a service (SaaS) applications.

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Astera

JANUARY 31, 2024

Its extensibility is evident through components, such as SQL Server Integration Services (SSIS) for ETL , SQL Server Reporting Services (SSRS) for creating and managing reports, SQL Server Analysis Service (SSAS) for analytical data models, and so on. Azure SQL Database).

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Insight Software

JULY 31, 2023

Financial and operational reporting teams may need to rely on IT personnel for report customization, data modeling, or resolving technical issues. That’s where we use the analytics side of Angles, when we’re able to do multiple loads throughout the day and pull that data out of EBS into our data warehouse.

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Data Warehouse Real-time Data Finance Data Modelling 52

Astera

JANUARY 2, 2024

Businesses, both large and small, find themselves navigating a sea of information, often using unhealthy data for business intelligence (BI) and analytics. Relying on this data to power business decisions is like setting sail without a map. This is why organizations have effective data management in place.

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Sisense

FEBRUARY 24, 2021

Challenge: CDOs need to provide a 360-degree view of data across multiple data silos and types of data sitting on the cloud, on-premises, or even in local drives. Many large organizations either have a central data warehouse or are in the process of creating one.

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Sisense

AUGUST 22, 2019

SQL is a critical skill for business intelligence. From accessing to transforming to reporting on data, SQL gives you the power to get the job done. These are the types of questions that take a customer to the next level of business intelligence — predictive analytics. . Python, R, and Analytics. A New Paradigm.

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