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.

Data Warehouse 52

Data Warehouse Big Data Data Quality Predictive Analytics 52

Astera

APRIL 27, 2023

Data Lake Vs Data Warehouse Every business needs to store, analyze, and make decisions based on data. To do this, they must choose between two popular data storage technologies: data lakes and data warehouses. What is a Data Lake? What is a Data Warehouse?

Data Warehouse 52

Data Warehouse Cloud Computing Data Management Data Governance 52

Tableau

MAY 11, 2022

At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location. Data fabric governance assumes a federated environment, so they scale by connecting to new data sources as they emerge.

Data Warehouse 98

Data Warehouse Government Artificial Intelligence Data Discovery 98

Tableau

MAY 11, 2022

At their most basic level, data fabrics leverage artificial intelligence and machine learning to unify and securely manage disparate data sources without migrating them to a centralized location. Data fabric governance assumes a federated environment, so they scale by connecting to new data sources as they emerge.

Data Warehouse 98

Data Warehouse Government Artificial Intelligence Data Discovery 98

Analysts Corner

JUNE 18, 2023

ETL Developer: Defining the Role An ETL developer is a professional responsible for designing, implementing, and managing ETL processes that extract, transform, and load data from various sources into a target data store, such as a data warehouse. Oracle, SQL Server, MySQL) Experience with ETL tools and technologies (e.g.,

Data Warehouse 130

Data Warehouse Data Quality Documentation Data Governance 130

Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Data integration.

Data Quality 102

Data Quality Government Artificial Intelligence Data Warehouse 102

Tableau

APRIL 18, 2022

Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Leverage semantic layers and physical layers to give you more options for combining data using schemas to fit your analysis. Data preparation. Data integration.

Data Quality 98

Data Quality Government Artificial Intelligence Data Warehouse 98

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 Data Quality Cloud Computing Monitoring 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.

Data Warehouse 52

Data Warehouse Data Quality Cloud Computing Monitoring 52

Sisense

OCTOBER 14, 2020

Today, data teams form a foundational element of startups and are an increasingly prominent part of growing existing businesses because they are instrumental in helping their companies analyze the huge volumes of data that they must deal with. Everyone wins!

Data Modelling 98

Data Modelling Embedded Analytics Data Warehouse Innovation 98

Domo

OCTOBER 22, 2021

Even though technology transformation is enabling accelerated progress in data engineering, analytics deployment, and predictive modeling to drive business value, deploying a data strategy across cloud systems remains inefficient and cumbersome for CIOs. One of the key obstacles is data access. The bottom line.

Data Warehouse 52

Data Warehouse Embedded Analytics Government Digital Transformation 52

Sisense

APRIL 7, 2020

In addition, this data lives in so many places that it can be hard to derive meaningful insights from it all. This is where analytics and data platforms come in: these systems, especially cloud-native Sisense, pull in data from wherever it’s stored ( Google BigQuery data warehouse , Snowflake , Redshift , etc.).

Big Data 86

Big Data Data Warehouse Product Management Product Management 86

The BAWorld

NOVEMBER 22, 2021

Data Modeling. Data modeling is a process used to define and analyze data requirements needed to support the business processes within the scope of corresponding information systems in organizations. Conceptual Data Model. Logical Data Model : It is an abstraction of CDM. Data Profiling.

Data Analytics 98

Data Analytics Data Analytics Data Warehouse Data Modelling 98

Astera

JULY 3, 2024

It creates a space for a scalable environment that can handle growing data, making it easier to implement and integrate new technologies. Moreover, a well-designed data architecture enhances data security and compliance by defining clear protocols for data governance.

Data Architecture 40

Data Architecture Data Warehouse Data Governance Government 40

Astera

NOVEMBER 30, 2023

With rising data volumes, dynamic modeling requirements, and the need for improved operational efficiency, enterprises must equip themselves with smart solutions for efficient data management and analysis. This is where Data Vault 2.0 It supersedes Data Vault 1.0, What is Data Vault 2.0? Data Vault 2.0

Data Warehouse 52

Data Warehouse Big Data Healthcare Data Governance 52

Actian

OCTOBER 7, 2019

A data hub is a logical architecture which enables data sharing by connecting producers of data (applications, processes, and teams) with consumers of data (other applications, process, and teams). Data hubs also simplify the data governance requirements as the data is persisted at a central location.

Data Warehouse 10

Data Warehouse Government Data Governance Agile 10

Dataversity

APRIL 5, 2021

However, most enterprises are hampered by data strategies that leave teams flat-footed when […]. The post Why the Next Generation of Data Management Begins with Data Fabrics appeared first on DATAVERSITY. Click to learn more about author Kendall Clark. The mandate for IT to deliver business value has never been stronger.

Data Management 244

Data Management Agile Data Warehouse Data Modelling 244

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.

Data Management 52

Data Management Data Warehouse Data Governance Data Quality 52

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

Actian

NOVEMBER 26, 2019

The Challenges of Connecting Disparate Data Sources and Migrating to a Cloud Data Warehouse. Migrating to a cloud data warehouse makes strategic sense in the modern context of cloud services and digital transformation. Conceptually, it is easy to understand why you would want to move to a cloud data warehouse.

Data Warehouse 8

Data Warehouse Vision Digital Transformation Digital 8

Astera

JANUARY 18, 2024

Fivetran is a low-code/no-code ELT (Extract, load and transform) solution that allows users to extract data from multiple sources and load it into the destination of their choice, such as a data warehouse. It also offers limited data transformation capabilities and that too through dbt core, which is an open source tool.

Data Warehouse 52

Data Warehouse Data Quality Data Management Government 52

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.

E-Commerce 52

E-Commerce Data Warehouse Retail Customer Experience 52

Cprime

MAY 4, 2023

Key Data Integration Use Cases Let’s focus on the four primary use cases that require various data integration techniques: Data ingestion Data replication Data warehouse automation Big data integration Data Ingestion The data ingestion process involves moving data from a variety of sources to a storage location such as a data warehouse or data lake.

Data Warehouse 52

Data Warehouse Big Data Data Quality Finance 52

Sisense

JULY 14, 2019

Hand in hand with your frontline personnel training, your company’s security policy should include an array of documents related to the use of data and company tech resources. Admins should be able to programmatically apportion user access by group, team, or individual, by data model, dataset, or down to the individual row-level.

Data Security 75

Data Security Data Modelling Documentation Cybersecurity 75

Actian

OCTOBER 7, 2019

A data hub is a logical architecture which enables data sharing by connecting producers of data (applications, processes, and teams) with consumers of data (other applications, process, and teams). Data hubs also simplify the data governance requirements as the data is persisted at a central location.

Data Warehouse 40

Data Warehouse Government Data Governance Agile 40

Astera

JULY 13, 2023

This process includes moving data from its original locations, transforming and cleaning it as needed, and storing it in a central repository. Data integration can be challenging because data can come from a variety of sources, such as different databases, spreadsheets, and data warehouses.

Data Warehouse 40

Data Warehouse Data Quality Data Governance Master Data Management 40

Astera

AUGUST 13, 2024

After modernizing and transferring the data, users access features such as interactive visualization, advanced analytics, machine learning, and mobile access through user-friendly interfaces and dashboards. The upgrade allows employees to access and analyze data easily, essential for quickly making informed business decisions.

Data Governance 52

Data Governance Data Architecture Data Quality Real-time Data 52

Astera

MARCH 28, 2024

So, organizations create a data governance strategy for managing their data, and an important part of this strategy is building a data catalog. They enable organizations to efficiently manage data by facilitating discovery, lineage tracking, and governance enforcement.

Data Governance 52

Data Governance Government Data Discovery Data Quality 52

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. Business/Data Analyst: The business analyst is all about the “meat and potatoes” of the business.

Data Quality 96

Data Quality Monitoring Big Data Government 96

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. It provides multiple security measures for data protection.

Data Warehouse 52

Data Warehouse Data Quality Data Management Data Security 52

Astera

AUGUST 16, 2024

The presence of diverse data assets requires organizations to plan, implement, and validate the source data during migration. Improper planning can lead to data corruption or loss. Astera offers an automated, no-code data solution that can make data migration cost-effective, simpler, and more accessible for organizations.

Digital Transformation 52

Digital Transformation Planning Government Cloud Computing 52

Dataversity

FEBRUARY 17, 2021

Click to learn more about author Steve Zagoudis. Successful problem solving requires finding the right solution to the right problem. We fail more often because we solve the wrong problem than because we get the wrong solution to the right problem.” – Russell L.

Data Warehouse 130

Data Warehouse Data Modelling Data Governance Data Quality 130

Astera

JUNE 3, 2024

Data warehouses Data warehouses are a specialized type of database designed for a specific purpose: large-scale data analysis. Data needs Data structure: Analyze the type of data the organization needs to store—structured, semi-structured, or unstructured.

Data Warehouse 52

Data Warehouse Documentation E-Commerce Data Management 52

Astera

MAY 13, 2024

It prepares data for analysis, making it easier to obtain insights into patterns and insights that aren’t observable in isolated data points. Once aggregated, data is generally stored in a data warehouse. Government: Using regional and administrative level demographic data to guide decision-making.

Supply Chain 52

Supply Chain E-Commerce Healthcare Data Quality 52

MindsMapped

JULY 18, 2020

Sitting in the gap between the Business/UX and the developers, Technical Business Analysts play an integral role in translation and governance. Business Analytics mostly work with data and statistics. They primarily synthesize data and capture insightful information through it by understanding its patterns. Business Analytics.

Business Analysis 46

Business Analysis Business Analysis Requirements Gathering Documentation 46

Astera

DECEMBER 22, 2022

It’s one of the three core data types, along with structured and semi-structured formats. Examples of unstructured data include call logs, chat transcripts, contracts, and sensor data, as these datasets are not arranged according to a preset data model. This makes managing unstructured data difficult.

Data Quality 52

Data Quality Data Mining Data Warehouse Data Governance 52

Astera

JANUARY 12, 2024

A solid data architecture is the key to successfully navigating this data surge, enabling effective data storage, management, and utilization. Enterprises should evaluate their requirements to select the right data warehouse framework and gain a competitive advantage.

Data Architecture 52

Data Architecture Data Warehouse Data Quality Government 52