Actian

JULY 17, 2019

If your company has existed for a number of years, then you likely have multiple databases, data marts and data warehouses, developed for independent business functions, that now must be integrated to provide the holistic perspective that digitally transformed business processes require. Why are distributed queries problematic?

Data Warehouse 40

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Astera

JANUARY 2, 2024

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. But what exactly is data management? What Is Data Management? As businesses evolve, so does their data.

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Astera

DECEMBER 16, 2022

These large data volumes present numerous data management challenges for companies, especially those with outdated management systems. Let’s explore the 7 data management challenges that tech companies face and how to overcome them. Data Management Challenges. See Case Sudy.

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Astera

DECEMBER 16, 2022

These large data volumes present numerous data management challenges for companies, especially those with outdated management systems. Let’s explore the 7 data management challenges that tech companies face and how to overcome them. Data Management Challenges. See Case Sudy.

Data Management 52

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Astera

DECEMBER 2, 2022

These large data volumes present numerous challenges for companies, especially those with outdated data management systems. Let’s explore the 7 data management challenges that tech companies face and how to overcome them. Data Management Challenges. Challenge#1: Accessing organizational data.

Data Management 52

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Astera

SEPTEMBER 25, 2024

The data management and integration world is filled with various software for all types of use cases, team sizes, and budgets. It provides many features for data integration and ETL. Generative AI Support: Airbyte provides access to LLM frameworks and supports vector data to power generative AI applications.

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Astera

DECEMBER 16, 2024

What is Hevo Data and its Key Features Hevo is a data pipeline platform that simplifies data movement and integration across multiple data sources and destinations and can automatically sync data from various sources, such as databases, cloud storage, SaaS applications, or data streaming services, into databases and data warehouses.

Data Warehouse 52

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Astera

JUNE 5, 2024

Streaming ETL is a modern approach to extracting, transforming, and loading (ETL) that processes and moves data from source to destination in real-time. It relies on real-time data pipelines that process events as they occur. Events refer to various individual pieces of information within the data stream.

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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

JANUARY 17, 2024

Common methods include Extract, Transform, and Load (ETL), Extract, Load, and Transform (ELT), data replication, and Change Data Capture (CDC). Each of these methods serves a unique purpose and is chosen based on factors such as the volume of data, the complexity of the data structures, and the need for real-time data availability.

Real-time Data 52

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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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Actian

DECEMBER 9, 2019

How Avalanche and DataConnect work together to deliver an end-to-end data management solution. Migrating to a cloud data warehouse makes strategic sense in the modern context of cloud services and digital transformation. Actian DataConnect and Actian Avalanche give you that end-to-end data management solution.

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Sisense

MARCH 20, 2019

Get ready data engineers, now you need to have both AWS and Microsoft Azure to be considered up-to-date. With most enterprise companies migrating to the cloud, having the knowledge of both these data warehouse platforms is a must. Data Warehousing. Hadoop : This is the main framework for processing Big Data.

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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’s New in Data Vault 2.0? Data Vault 2.0

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Astera

JANUARY 15, 2024

Push-down ELT technology: Matillion utilizes push-down ELT technology, which pushes transformations down to the data warehouse for efficient processing. Automation and scheduling: You can automate data pipelines and schedule them to run at specific times. Data quality checks and data profiling.

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Domo

SEPTEMBER 11, 2019

2 – Customers find it easy and inexpensive to get data in and out of Domo Other data management solutions might make it easy to get your data in, but they make it difficult and/or expensive to get it out. It’s a great primer for anyone contemplating going down this increasingly popular road.

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The BAWorld

SEPTEMBER 18, 2024

Load : The formatted data is then transferred into a data warehouse or another data storage system. ELT (Extract, Load, Transform) This method proves to be efficient when both your data source and target reside within the same ecosystem. Extract: Data is pulled from its source.

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Actian

DECEMBER 2, 2019

Checklist: Critical Capabilities to Consider when Selecting a Data Integration Vendor That Enables Real-Time Analytics Use Cases. Migrating to a cloud data warehouse makes strategic sense in the modern context of cloud services and digital transformation.

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Astera

DECEMBER 18, 2023

However, as data volumes continue to grow and the need for real-time insights increases, banks are pushed to embrace more agile data management strategies. Change data capture (CDC) emerges as a pivotal solution that enables real-time data synchronization and analysis. daily or weekly).

Finance 52

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Astera

JUNE 6, 2023

The pipeline includes stages such as data ingestion, extraction, transformation, validation, storage, analysis, and delivery. Technologies like ETL, batch processing, real-time streaming, and data warehouses are used. They are ideal for handling historical data analysis, offline reporting, and batch-oriented tasks.

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Astera

MAY 30, 2023

Harness the Power of No-Code Data Pipelines As businesses continue to accumulate data at an unprecedented rate, the need for efficient and effective data management solutions has become more critical than ever before. This step involves a range of operations, such as data mapping, data cleansing, and data enrichment.

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Astera

SEPTEMBER 28, 2023

Reverse ETL is a relatively new concept in the field of data engineering and analytics. It’s a data integration process that involves moving data from a data warehouse, data lake, or other analytical storage systems back into operational systems, applications, or databases that are used for day-to-day business operations.

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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.

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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. What is Data-First Modernization? It involves a series of steps to upgrade data, tools, and infrastructure.

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Astera

SEPTEMBER 20, 2023

What is Data Integration? Data integration is a core component of the broader data management process, serving as the backbone for almost all data-driven initiatives. It ensures businesses can harness the full potential of their data assets effectively and efficiently. How Does Data Integration Work?

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Astera

SEPTEMBER 20, 2023

What is Data Integration? Data integration is a core component of the broader data management process, serving as the backbone for almost all data-driven initiatives. It ensures businesses can harness the full potential of their data assets effectively and efficiently. How Does Data Integration Work?

Data Warehouse 52

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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

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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. Stitch also offers solutions for non-technical teams to quickly set up data pipelines.

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Astera

JANUARY 24, 2024

What is Change Data Capture? Change Data Capture (CDC) is a technique used in data management to identify and track changes made to data in a database, and applying those changes to the target system. Below is the step-by-step explanation on how change data capture typically works.

E-Commerce 52

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Actian

FEBRUARY 24, 2020

If you want your business to be agile, you need to be leveraging real-time data. The meaningful signals in the data get drowned out by the noise, and before long, decision-makers stop using the data entirely. This is what happens when big data isn’t managed – it becomes clutter. Actian can help.

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Astera

SEPTEMBER 4, 2023

This scalability is particularly beneficial for growing businesses that experience increasing data traffic. Enable Real-time Analytics: Data replication tools continuously synchronize data across all systems, ensuring that analytics tools always work with real-time data.

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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

DECEMBER 28, 2023

Informatica, one of the key players in the data integration space, offers a comprehensive suite of tools for data management and governance. In this article, we are going to explore the top 10 Informatica alternatives so you can select the best data integration solution for your organization. What Is Informatica?

IBM cost 52

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Astera

DECEMBER 28, 2023

Informatica, one of the key players in the data integration space, offers a comprehensive suite of tools for data management and governance. In this article, we are going to explore the top 10 Informatica alternatives so you can select the best data integration solution for your organization. What Is Informatica?

IBM cost 52

IBM cost IBM Support IBM Maintenance Data Warehouse 52

Actian

DECEMBER 9, 2018

Do you find your data is slowing your decision-making processes and preventing you from being truly agile? Imagine what you could do if you were to harness the power of real-time data. Modern businesses operate in a constantly changing, intensely complex and data-rich environment.

Data Warehouse 40

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Astera

JUNE 13, 2023

Different Types of Data Pipelines: Batch Data Pipeline: Processes data in scheduled intervals, ideal for non-real-time analysis and efficient handling of large data volumes. Real-time Data Pipeline: Handles data in a streaming fashion, essential for time-sensitive applications and immediate insights.

Data Warehouse 52

Data Warehouse Data Quality Real-time Data Visualization 52

Astera

JUNE 13, 2023

Different Types of Data Pipelines: Batch Data Pipeline: Processes data in scheduled intervals, ideal for non-real-time analysis and efficient handling of large data volumes. Real-time Data Pipeline: Handles data in a streaming fashion, essential for time-sensitive applications and immediate insights.

Data Warehouse 52

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