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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Astera

DECEMBER 21, 2022

For this reason, most organizations today are creating cloud data warehouse s to get a holistic view of their data and extract key insights quicker. What is a cloud data warehouse? Moreover, when using a legacy data warehouse, you run the risk of issues in multiple areas, from security to compliance.

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Astera

APRIL 3, 2024

What is a data quality framework? A data quality framework is a set of guidelines that enable you to measure, improve, and maintain the quality of data in your organization. It’s not a magic bullet—data quality is an ongoing process, and the framework is what provides it a structure.

Data Quality 52

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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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Data Warehouse Data Management Data Modelling Data Quality 52

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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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.

Data Quality 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. Step 2: Data Mapping and Profiling This step involves understanding the relationships between data elements from different systems.

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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.

Data Warehouse 52

Data Warehouse Data Quality Documentation Data Management 52

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.

Data Warehouse 52

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Astera

JUNE 5, 2024

In conventional ETL , data comes from a source, is stored in a staging area for processing, and then moves to the destination (data warehouse). In streaming ETL, the source feeds real-time data directly into a stream processing platform. It can be an event-based application, a web lake, a database , or a data warehouse.

Real-time 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. 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

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 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. The combination of data vault and information marts solves this problem.

Agile 52

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Astera

AUGUST 13, 2024

Data-first modernization is a strategic approach to transforming an organization’s data management and utilization. It involves making data the center and organizing principle of the business by centralizing data management, prioritizing data quality , and integrating data into all business processes.

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Astera

MARCH 30, 2023

It is an integral aspect of data management within an organization as it enables the stakeholders to access and utilize relevant data sets for analysis, decision making, and other purposes. It involve multiple forms, depending on the requirements and objectives of stakeholders.

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Data Quality Data Warehouse Data Security Finance 52

Astera

MAY 25, 2023

Overcoming Snowflake Migration Challenges: A Complete Guide Snowflake has quickly become a popular choice for data warehousing, thanks to its cloud-native architecture, advanced features, and scalability. However, migration to Snowflake from an existing data warehouse platform can be a complex and challenging process.

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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

SEPTEMBER 28, 2023

Data Quality: ETL facilitates data quality management , crucial for maintaining a high level of data integrity, which, in turn, is foundational for successful analytics and data-driven decision-making. Reverse ETL is a relatively new concept in the field of data engineering and analytics.

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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

OCTOBER 31, 2023

At its core, it is a set of processes and tools that enables businesses to extract raw data from multiple source systems, transform it to fit their needs, and load it into a destination system for various data-driven initiatives. The target system is most commonly either a database, a data warehouse, or a data lake.

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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

DECEMBER 16, 2022

IoT systems are another significant driver of Big Data. Many businesses move their data to the cloud to overcome this problem. Cloud-based data warehouses are becoming increasingly popular for storing large amounts of data. Challenge#4: Analyzing unstructured data. Challenge#5: Maintaining data quality.

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Astera

DECEMBER 16, 2022

IoT systems are another significant driver of Big Data. Many businesses move their data to the cloud to overcome this problem. Cloud-based data warehouses are becoming increasingly popular for storing large amounts of data. Challenge#4: Analyzing unstructured data. Challenge#5: Maintaining data quality.

Data Management 52

Data Management Data Warehouse Big Data Procurement 52

Astera

DECEMBER 2, 2022

IoT systems are another significant driver of Big Data. Many businesses move their data to the cloud to overcome this problem. Cloud-based data warehouses are becoming increasingly popular for storing large amounts of data. Challenge#4: Analyzing unstructured data. Challenge#5: Maintaining data quality.

Data Management 52

Data Management Data Warehouse Big Data Procurement 52

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

AUGUST 12, 2024

Financial data integration faces many challenges that hinder its effectiveness and efficiency in detecting and preventing fraud. Challenges of Financial Data Integration Data Quality and Availability Data quality and availability are crucial for financial data integration project, especially detecting fraud.

IBM Support 52

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Astera

DECEMBER 18, 2023

For instance, if the extracted data contains missing values or outliers, these issues are addressed during the transformation process to ensure data accuracy. Finally, the transformed data is loaded into a target system or data warehouse for reporting and analysis. Sign up for a demo or a 14-day- free trial now!

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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

JANUARY 19, 2024

Shortcomings in Complete Data Management : While MuleSoft excels in integration and connectivity, it falls short of being an end-to-end data management platform. Notably, MuleSoft lacks built-in capabilities for AI-powered data extraction and the direct construction of data warehouses. So what are you waiting for?

IBM Support 52

IBM Support IBM cost IBM Maintenance Data Warehouse 52

Astera

FEBRUARY 9, 2024

Financial data integration faces many challenges that hinder its effectiveness and efficiency in detecting and preventing fraud. Challenges of Financial Data Integration Data Quality and Availability Data quality and availability are crucial for any data integration project, especially for fraud detection.

IBM Support 52

IBM Support Data Quality IBM Maintenance IBM cost 52

Astera

DECEMBER 21, 2022

Understanding data extraction and why it is significant for organizations to extract insights from data? What are the key features of a data extraction tool? Extract Data from Unstructured Documents with ReportMiner. What is Data Extraction? Enhanced Data Quality. Read on to find out.

Data Mining 52

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Astera

MARCH 16, 2023

So, whether you want to analyze just a portion of your data or get a holistic view of your entire data, you don’t have to worry about delays, bottlenecks, or performance issues. Embrace semi and unstructured data Unlike relational databases or data warehouses, cloud data lakes are not restricted by a rigid schema.

Big Data 52

Big Data Data Warehouse Data Quality Data Analytics 52

Astera

APRIL 7, 2023

Data Integration – the process of collecting and combining data from multiple data sources to create a unified data view. Data Storage – a process of storing and managing the collected data in a data warehouse or a database repository.

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