Remove Business Intelligence Remove Comparison Remove Data Engineering Remove Scalability
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Supply Chain Analytics: Opportunities in Data Analysis and Business Intelligence

Altexsoft

diversity of sales channels, complex structure resulting in siloed data and lack of visibility. These challenges can be addressed by intelligent management supported by data analytics and business intelligence (BI) that allow for getting insights from available data and making data-informed decisions to support company development.

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ELT Process: Key Components, Benefits, and Tools to Build ELT Pipelines

Altexsoft

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Scalability. ELT vs ETL.

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ETL vs ELT: Key Differences Everyone Must Know

Altexsoft

From the late 1980s, when data warehouses came into view, and up to the mid-2000s, ETL was the main method used in creating data warehouses to support business intelligence (BI). As data keeps growing in volumes and types, the use of ETL becomes quite ineffective, costly, and time-consuming. What is ELT?

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Altexsoft - Untitled Article

Altexsoft

Before jumping into the comparison of available products right away, it will be a good idea to get acquainted with the data warehousing basics first. What is a data warehouse? It is usually created and used primarily for data reporting and analysis purposes. Scalability opportunities. Scalability.

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The Good and the Bad of Snowflake Data Warehouse

Altexsoft

Not long ago setting up a data warehouse — a central information repository enabling business intelligence and analytics — meant purchasing expensive, purpose-built hardware appliances and running a local data center. BTW, we have an engaging video explaining how data engineering works.

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IBM InfoSphere vs Oracle Data Integrator vs Xplenty and Others: Data Integration Tools Compared

Altexsoft

Not to mention that they require a decent level of expertise to develop, deploy, and maintain data integration flows. Now that you have a general picture of what data integration tools are, let’s move to the comparison of popular vendors. How to choose data integration software: key comparison criteria.

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The Good and the Bad of Apache Spark Big Data Processing

Altexsoft

Its flexibility allows it to operate on single-node machines and large clusters, serving as a multi-language platform for executing data engineering , data science , and machine learning tasks. Before diving into the world of Spark, we suggest you get acquainted with data engineering in general.