Astera

APRIL 28, 2023

Data management can be a daunting task, requiring significant time and resources to collect, process, and analyze large volumes of information. Continuous Data Quality Monitoring According to Gartner , poor data quality cost enterprises an average of $15 million per year.

E-Commerce 40

E-Commerce Supply Chain Artificial Intelligence Data Warehouse 40

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

Agile Data Warehouse Data Quality Government 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.

Data Warehouse 52

Data Warehouse Documentation Monitoring Data Quality 52

Astera

JANUARY 24, 2024

Modern data management relies heavily on ETL (extract, transform, load) procedures to help collect, process, and deliver data into an organization’s data warehouse. However, ETL is not the only technology that helps an enterprise leverage its data. Considering cloud-first data management?

Data Warehouse 52

Data Warehouse Data Quality Data Management Data Security 52

Astera

MARCH 28, 2024

Data Catalog vs. Data Dictionary A common confusion arises when data dictionaries come into the discussion. Both data catalog and data dictionary serve essential roles in data management. This functionality includes data definitions, schema details, data lineage, and usage statistics.

Data Governance 52

Data Governance Government Data Discovery Data Quality 52

Astera

MARCH 30, 2023

At the fundamental level, data sharing is the process of making a set of data resources available to individuals, departments, business units or even other organizations. This makes it difficult to integrate and consolidate data from various departments, resulting in issues with data quality and delays in data processing.

Data Quality 52

Data Quality Data Warehouse Data Security Finance 52

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.

Data Warehouse 52

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

Astera

DECEMBER 7, 2023

ETL refers to a process used in data integration and warehousing. It gathers data from various sources, transforms it into a consistent format, and then loads it into a target database, data warehouse , or data lake. Extract: Gather data from various sources like databases, files, or web services.

Supply Chain 52

Supply Chain E-Commerce Data Warehouse Real-time Data 52

Astera

NOVEMBER 27, 2023

Clean and accurate data is the foundation of an organization’s decision-making processes. However, studies reveal that only 3% of the data in an organization meets basic data quality standards, making it necessary to prepare data effectively before analysis. This is where data profiling comes into play.

Data Mining 52

Data Mining Data Quality Data Governance Business Intelligence 52

Astera

DECEMBER 7, 2023

ETL refers to a process used in data warehousing and integration. It gathers data from various sources, transforms it into a consistent format, and then loads it into a target database, data warehouse, or data lake. Extract: Gather data from various sources like databases, files, or web services.

Supply Chain 52

Supply Chain E-Commerce Real-time Data Data Warehouse 52

Astera

MARCH 31, 2023

In today’s data-driven world, businesses rapidly generate massive amounts of data. Managing this data effectively and timely is critical for decision-making, but how can they make sense of all this data most efficiently? 2. Ensuring data quality Another major challenge is improving data quality.

Data Quality 52

Data Quality Data Security Data Warehouse Monitoring 52

Astera

MAY 6, 2024

Whether it’s choosing the right marketing strategy, pricing a product, or managing supply chains, data mining impacts businesses in various ways: Finance : Banks use predictive models to assess credit risk, detect fraudulent transactions, and optimize investment portfolios. Data quality is a priority for Astera.

Data Mining 52

Data Mining Supply Chain Data Quality Visualization 52

GAVS Technology

JUNE 2, 2021

In the recently announced Technology Trends in Data Management, Gartner has introduced the concept of “Data Fabric”. Here is the link to the document, Top Trends in Data and Analytics for 2021: Data Fabric Is the Foundation (gartner.com). What is Data Fabric? Data Virtualization. Data Lakes.

Healthcare 52

Healthcare Data Governance Government Data Mining 52

Astera

MARCH 26, 2023

But managing this data can be a significant challenge, with issues ranging from data volume to quality concerns, siloed systems, and integration difficulties. In this blog, we’ll explore these common data management challenges faced by insurance companies.

Documentation 52

Documentation Data Quality Data Warehouse Data Management 52

Astera

JUNE 13, 2023

Acting as a conduit for data, it enables efficient processing, transformation, and delivery to the desired location. By orchestrating these processes, data pipelines streamline data operations and enhance data quality. Stream processing platforms handle the continuous flow of data, enabling real-time insights.

E-Commerce 52

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

Astera

JUNE 14, 2023

The ultimate goal is to convert unstructured data into structured data that can be easily housed in data warehouses or relational databases for various business intelligence (BI) initiatives. Healthcare Obtaining accurate healthcare data is especially important as it can impact patient outcomes.

Documentation 52

Documentation Supply Chain Healthcare Logistics 52

Insight Software

MAY 9, 2024

The key components of a data pipeline are typically: Data Sources : The origin of the data, such as a relational database , data warehouse, data lake , file, API, or other data store. This can include tasks such as data ingestion, cleansing, filtering, aggregation, or standardization.

IBM Maintenance 52

IBM Maintenance Data Warehouse IBM Support Real-time Data 52