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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Data Warehouse Data Management Data Modelling Data Quality 52

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

JANUARY 1, 2024

What Is Data Quality? Data quality is the measure of data health across several dimensions, such as accuracy, completeness, consistency, reliability, etc. In short, the quality of your data directly impacts the effectiveness of your decisions.

Data Quality 52

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Astera

JANUARY 1, 2024

What Is Data Quality? Data quality is the measure of data health across several dimensions, such as accuracy, completeness, consistency, reliability, etc. In short, the quality of your data directly impacts the effectiveness of your decisions.

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Astera

APRIL 23, 2024

To do so, they need data quality metrics relevant to their specific needs. Organizations use data quality metrics, also called data quality measurement metrics, to assess the different aspects, or dimensions, of data quality within a data system and measure the data quality against predefined standards and requirements.

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Astera

AUGUST 8, 2023

What matters is how accurate, complete and reliable that data. Data quality is not just a minor detail; it is the foundation upon which organizations make informed decisions, formulate effective strategies, and gain a competitive edge. to help clean, transform, and integrate your data.

Data Quality 52

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Business Analysis Knowledge Share

OCTOBER 24, 2023

Data Warehousing is the process of collecting, storing, and managing data from various sources into a central repository. This repository, often referred to as a data warehouse , is specifically designed for query and analysis. Data Sources Data Warehouses collect data from diverse sources within an organization.

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Astera

JUNE 14, 2023

What is Document Data Extraction? Document data extraction refers to the process of extracting relevant information from various types of documents, whether digital or in print. The process enables businesses to unlock valuable information hidden within unstructured documents.

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Astera

DECEMBER 28, 2023

Airbyte vs Fivetran vs Astera: Overview Airbyte Finally, Airbyte is primarily an open-source data replication solution that leverages ELT to replicate data between applications, APIs, data warehouses, and data lakes. Like other data integration platforms , Airbyte features a visual UI with built-in connectors.

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Astera

DECEMBER 28, 2023

Airbyte vs Fivetran vs Astera: Overview Airbyte Finally, Airbyte is primarily an open-source data replication solution that leverages ELT to replicate data between applications, APIs, data warehouses, and data lakes. Like other data integration platforms , Airbyte features a visual UI with built-in connectors.

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Data Warehouse Data Quality Documentation Data Management 52

Astera

FEBRUARY 27, 2024

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

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Astera

FEBRUARY 27, 2024

52% of IT experts consider faster analytics essential to data warehouse success. However, scaling your data warehouse and optimizing performance becomes more difficult as data volume grows. Leveraging data warehouse best practices can help you design, build, and manage data warehouses more effectively.

Data Warehouse 52

Data Warehouse Cloud Computing Data Quality Monitoring 52

Astera

FEBRUARY 26, 2024

Azure SQL Data Warehouse, now called Azure Synapse Analytics, is a powerful analytics and BI platform that enables organizations to process and analyze large volumes of data in a centralized place. However, this data is often scattered across different systems, making it difficult to consolidate and utilize effectively.

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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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Business Analysis Knowledge Share

MARCH 15, 2023

Understanding the key concepts of data warehousing, such as data integration, dimensional modeling, OLAP, and data marts, is vital for business analysts who are responsible for analyzing data and providing insights that drive business performance. What is Data Warehousing?

Big Data 52

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Astera

FEBRUARY 9, 2024

The data is stored in different locations, such as local files, cloud storage, databases, etc. The data is updated at different frequencies, such as daily, weekly, monthly, etc. The data quality is inconsistent, such as missing values, errors, duplicates, etc.

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Astera

FEBRUARY 9, 2024

The data is stored in different locations, such as local files, cloud storage, databases, etc. The data is updated at different frequencies, such as daily, weekly, monthly, etc. The data quality is inconsistent, such as missing values, errors, duplicates, etc. The validation process should check the accuracy of the CCF.

Banking 52

Banking Data Warehouse Data Quality Data Security 52

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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Astera

DECEMBER 9, 2024

Data Governance is a systematic approach to managing and utilizing an organizations data. It ensures data quality, security, and accessibility for informed decision-making. However, managing, analyzing, and governing the data is a complex process.

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Astera

SEPTEMBER 25, 2024

It provides many features for data integration and ETL. While Airbyte is a reputable tool, it lacks certain key features, such as built-in transformations and good documentation. Limited documentation: Many third-party reviews mention Airbyte lacks adequate connector-related documentation. Govern their data assets.

Real-time Data 52

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Astera

AUGUST 29, 2024

It enables easy data sharing and collaboration across teams, improving productivity and reducing operational costs. Identifying Issues Effective data integration manages risks associated with M&A. Assessment includes understanding data ownership, usage, and dependencies.

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Astera

MARCH 11, 2024

It eliminates the need for complex infrastructure management, resulting in streamlined operations. According to a recent Gartner survey, 85% of enterprises now use cloud-based data warehouses like Snowflake for their analytics needs. What are Snowflake ETL Tools? Snowflake ETL tools are not a specific category of ETL tools.

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Astera

MARCH 26, 2023

Common Data Management Challenges in the Insurance Industry Data trapped in Unstructured sources Managing the sheer volume of data scattered across various unstructured sources is one of the top data management challenges in the insurance industry. These PDFs may vary in format and layout.

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Astera

APRIL 24, 2024

There are different types of data ingestion tools, each catering to the specific aspect of data handling. Standalone Data Ingestion Tools : These focus on efficiently capturing and delivering data to target systems like data lakes and data warehouses.

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Astera

JANUARY 2, 2024

Free Download Here’s what the data management process generally looks like: Gathering Data: The process begins with the collection of raw data from various sources. Once collected, the data needs a home, so it’s stored in databases, data warehouses , or other storage systems, ensuring it’s easily accessible when needed.

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Astera

SEPTEMBER 20, 2023

Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks. Enforces data quality standards through transformations and cleansing as part of the integration process.

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Astera

SEPTEMBER 20, 2023

Data movement involves data transformation, cleansing, formatting, and standardization. Data Quality Consideration Emphasis is on data availability rather than extensive data quality checks. Enforces data quality standards through transformations and cleansing as part of the integration process.

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Data Warehouse Data Quality Real-time Data E-Commerce 52

Data Pine

JANUARY 24, 2021

This can include a multitude of processes, like data profiling, data quality management, or data cleaning, but we will focus on tips and questions to ask when analyzing data to gain the most cost-effective solution for an effective business strategy. 4) How can you ensure data quality?

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Astera

MARCH 20, 2024

Enterprise data management (EDM) is a holistic approach to inventorying, handling, and governing your organization’s data across its entire lifecycle to drive decision-making and achieve business goals. It provides a strategic framework to manage enterprise data with the highest standards of data quality , security, and accessibility.

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Astera

JANUARY 18, 2024

Get data extraction, transformation, integration, warehousing, and API and EDI management with a single platform. Talend is a data integration solution that focuses on data quality to deliver reliable data for business intelligence (BI) and analytics. Pros: Support for multiple data sources and destinations.

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Astera

JANUARY 11, 2024

Siloed Data Challenges Financial institutions face several hurdles due to decentralized data. These challenges include: Legacy Systems: Outdated systems make it difficult to get the best data into your data warehouse. Divergent data sources can lead to conflicting information, undermining accuracy and reliability.

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Astera

JUNE 6, 2023

As the volume and complexity of data continue to rise, effective management and processing become essential. The best data pipeline tools offer the necessary infrastructure to automate data workflows, ensuring impeccable data quality, reliability, and timely availability.

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Astera

FEBRUARY 20, 2023

Data without standardization can result in duplicate records, system failure, and inaccurate insights, which can affect patient care. The use of different data formats by healthcare providers hinders communication and data sharing between systems, which leads to and data quality issues.

Healthcare 52

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Astera

JUNE 6, 2023

The transformation layer applies cleansing, filtering, and data manipulation techniques, while the loading layer transfers the transformed data to a target repository, such as a data warehouse or data lake. Types of ETL Architectures Batch ETL Architecture: Data is processed at scheduled intervals.

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Astera

FEBRUARY 16, 2024

There are several ETL tools written in Python that leverage Python libraries for extracting, loading and transforming diverse data tables imported from multiple data sources into data warehouses. Astera Aspect Astera Python Data Integration Supports various data sources and destinations with ease.

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Astera

MARCH 13, 2023

Modern organizations must process information from numerous data sources , including applications, databases , and data warehouses , to gain trusted insights and build a sustainable competitive advantage. Moreover, such an undertaking almost always puts data quality at high risk.

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