Astera

NOVEMBER 14, 2023

If you have had a discussion with a data engineer or architect on building an agile data warehouse design or maintaining a data warehouse architecture, you’d probably hear them say that it is a continuous process and doesn’t really have a definite end. What do you need to build an agile data warehouse?

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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

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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Astera

APRIL 7, 2023

Finally, the stored data is retrieved at optimal speeds to support efficient analysis and decision-making. Essentially, a data warehouse also acts as a centralized database for storing structured, analysis-ready data and giving a holistic view of this data to decision-makers.

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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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The BAWorld

FEBRUARY 18, 2025

Power BI has become a go-to tool in the business intelligence (BI) and data analytics field, allowing companies to convert raw data into actionable reports and dashboards. Works with datasets to discover trends and insights, maintaining data accuracy. ollaborates with analysts and IT teams to provide smooth data flow.

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Sisense

JULY 8, 2020

Dealing with Data is your window into the ways Data Teams are tackling the challenges of this new world to help their companies and their customers thrive. In recent years we’ve seen data become vastly more available to businesses. This has allowed companies to become more and more data driven in all areas of their business.

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Astera

MAY 31, 2024

Data Science vs. Data Analytics Organizations increasingly use data to gain a competitive edge. Two key disciplines have emerged at the forefront of this approach: data science vs data analytics. In contrast, data science enables you to create data-driven algorithms to forecast future outcomes.

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Actian

JULY 30, 2019

Data warehouse modernization. Move your Netezza, Teradata or Exadata data warehouse that likely runs on obsolete, proprietary hardware to a shiny new system that runs in the cloud, costs a fraction of what you were paying before and can be turned on and off like a light switch. Sounds like a no-brainer. Is it that easy?

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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.,

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Astera

MAY 30, 2024

What Is Data Analytics? Data analytics is the science of analyzing raw data to draw conclusions about it. The process involves examining extensive data sets to uncover hidden patterns, correlations, and other insights. Data Mining : Sifting through data to find relevant information.

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Actian

JUNE 4, 2019

Implementing a data warehouse is a big investment for most companies and the decisions you make now will impact both your IT costs and the business value you are able to create for many years. Data Warehouse Cost. Your data warehouse is the centralized repository for your company’s data assets.

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Sisense

JANUARY 15, 2021

The modern data team has gained traction in large part thanks to the startups in Silicon Valley that have put an emphasis on collecting, analyzing, and commoditizing data. These younger companies have invested in talent with specific data science skills, particularly with code-driven data analytics.

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Astera

MAY 25, 2023

Unlocking the Potential of Amazon Redshift Amazon Redshift is a powerful cloud-based data warehouse that enables quick and efficient processing and analysis of big data. Amazon Redshift can handle large volumes of data without sacrificing performance or scalability. What Is Amazon Redshift? 

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Tableau

APRIL 18, 2022

Monitor data sources according to policies you customize to help users know if fresh, quality data is ready for use. Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Data preparation. Data integration. Orchestration.

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Tableau

APRIL 18, 2022

Monitor data sources according to policies you customize to help users know if fresh, quality data is ready for use. Shine a light on who or what is using specific data to speed up collaboration or reduce disruption when changes happen. Data modeling. Data preparation. Data integration. Orchestration.

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Astera

APRIL 27, 2023

This is where data extraction tools from companies like Matillion, Astera , and Fivetran are used to organize and prepare data for a cloud data warehouse. ELT or ETL tools , such as DBT, work within a cloud data warehouse to convert, clean, and structure data, into a format usable by data engineers and analysts.

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Sisense

AUGUST 22, 2019

What’s been missing is a way to natively integrate Python and R with the rest of the data analytics stack. Database access and data modeling in SQL should happen within the same platform that Python and R are used so that analysts can rapidly iterate on both datasets and models simultaneously. A New Paradigm.

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Sisense

MARCH 14, 2021

However, when investigating big data from the perspective of computer science research, we happily discover much clearer use of this cluster of confusing concepts. As we move from right to left in the diagram, from big data to BI, we notice that unstructured data transforms into structured data.

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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.

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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.

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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.

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Astera

APRIL 28, 2023

To address these challenges, approximately 44% of companies are planning to invest in artificial intelligence (AI) to streamline their data warehousing processes and improve the accuracy of their insights. AI is a powerful tool that goes beyond traditional data analytics.

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Actian

MAY 23, 2019

Because data is separated and fragmented, decision makers (at all levels of the organization) are prevented from seeing the holistic big picture of how their actions and decisions impact the company. Data silos and company culture. The next step to break down data silos is to establish a unified company-wide data model.

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Actian

JUNE 17, 2019

The key to converting data into actionable insights is having the right set of tools and a structured method for processing data through a value stream to generate progressive levels of refinement. There are only 2 levels of refinement (aggregation into the warehouse and curation into reports) occurring.

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Astera

SEPTEMBER 20, 2024

his setup allows users to access and manage their data remotely, using a range of tools and applications provided by the cloud service. Cloud databases come in various forms, including relational databases, NoSQL databases, and data warehouses. There are several types of NoSQL databases, including document stores (e.g.,

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Astera

AUGUST 22, 2023

Uncover hidden insights and possibilities with Generative AI capabilities and the new, cutting-edge data analytics and preparation add-ons We’re excited to announce the release of Astera 10.3—the the latest version of our enterprise-grade data management platform.

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Actian

JULY 11, 2019

Data science professionals have been working with companies and individual technology providers for many years to determine a scalable and efficient method to aggregate data from diverse data sources. The post Will Data Management in Operational Technology Finally Be Standardized?

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The BAWorld

SEPTEMBER 2, 2021

Non-technical users can also work easily with structured data. Structured Data Example. can be grouped in a data warehouse for marketing analysis. This is a classic example of structured data and can be efficiently managed through a database. Unstructured Data. Let us explore some examples.

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Actian

MAY 21, 2019

The refinement process starts with the ingestion and aggregation of data from each of the source systems. This is often done in some sort of data warehouse. Once the data is in a common place, it must be merged and reconciled into a common data model – addressing, for example, duplication, gaps, time differences and conflicts.

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Data Pine

MARCH 31, 2022

This could involve anything from learning SQL to buying some textbooks on data warehouses. A data scientist has a similar role as the BI analyst, however, they do different things. While analysts focus on historical data to understand current business performance, scientists focus more on data modeling and prescriptive analysis.

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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.

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Astera

JUNE 8, 2023

Data Integrity and Concurrency Control: Oracle ensures data integrity through constraints, triggers, and advanced concurrency control techniques. Data Analytics and Business Intelligence: Oracle supports powerful data analytics and business intelligence, enabling robust analysis, reporting, and decision-making.

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Astera

JUNE 8, 2023

Data Integrity and Concurrency Control: Oracle ensures data integrity through constraints, triggers, and advanced concurrency control techniques. Data Analytics and Business Intelligence: Oracle supports powerful data analytics and business intelligence, enabling robust analysis, reporting, and decision-making.

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Astera

AUGUST 28, 2024

However, with the abundance of different types of data analysis tools in the market, what was supposed to be a simple task has become a complex undertaking. This article aims to simplify the process of finding the data analytics platform that meets your organization’s specific needs.

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Sisense

MAY 21, 2020

Net new use cases allow new and better access to tools that help democratize insights to enable better decision making,” says Naveen Punjabi, lead for data analytics partnerships at Google Cloud. “We Data Freedom is a key focus and by better understanding customer needs, we will create more packaged solutions.”.

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