Data Science Blog

SEPTEMBER 19, 2023

So why using IaC for Cloud Data Infrastructures? This ensures that the data models and queries developed by data professionals are consistent with the underlying infrastructure. Enhanced Security and Compliance Data Warehouses often store sensitive information, making security a paramount concern.

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Dataversity

MARCH 29, 2021

Have you ever been in a situation when you had to represent the ETL team by being up late for L3 support only to find out that one of your […]. The post Rethinking Extract Transform Load (ETL) Designs appeared first on DATAVERSITY.

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phData

SEPTEMBER 27, 2024

Introduction: The Customer Data Modeling Dilemma You know, that thing we’ve been doing for years, trying to capture the essence of our customers in neat little profile boxes? For years, we’ve been obsessed with creating these grand, top-down customer data models. Yeah, that one.

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AWS Machine Learning Blog

FEBRUARY 21, 2025

Apache Hive was used to provide a tabular interface to data stored in HDFS, and to integrate with Apache Spark SQL. Apache HBase was employed to offer real-time key-based access to data. Data is stored in HDFS and is accessed via Hive, which provides a tabular interface to the data and integrates with Spark SQL.

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Pickl AI

JUNE 7, 2024

Summary: Choosing the right ETL tool is crucial for seamless data integration. Top contenders like Apache Airflow and AWS Glue offer unique features, empowering businesses with efficient workflows, high data quality, and informed decision-making capabilities. Choosing the right ETL tool is crucial for smooth data management.

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The Data Administration Newsletter

APRIL 6, 2021

A metadata-driven data warehouse (MDW) offers a modern approach that is designed to make EDW development much more simplified and faster. It makes use of metadata (data about your data) as its foundation and combines data modeling and ETL functionalities to build data warehouses.

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phData

OCTOBER 9, 2023

In this blog, we explore best practices and techniques to optimize Snowflake’s performance for data vault modeling , enabling your organizations to achieve efficient data processing, accelerated query performance, and streamlined ETL workflows.

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Smart Data Collective

OCTOBER 20, 2021

The upsurge of data (with the introduction of non-traditional data sources like streaming data, machine logs, etc.) along with traditional ones challenge old models of data integration. Legacy solutions lack precision and speed while handling big data.

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Dataconomy

SEPTEMBER 4, 2023

Key features of cloud analytics solutions include: Data models , Processing applications, and Analytics models. Data models help visualize and organize data, processing applications handle large datasets efficiently, and analytics models aid in understanding complex data sets, laying the foundation for business intelligence.

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ODSC - Open Data Science

JUNE 12, 2023

This article is an excerpt from the book Expert Data Modeling with Power BI, Third Edition by Soheil Bakhshi, a completely updated and revised edition of the bestselling guide to Power BI and data modeling. Then we have some other ETL processes to constantly land the past 5 years of data into the Datamarts.

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Pickl AI

JULY 3, 2023

It is the process of converting raw data into relevant and practical knowledge to help evaluate the performance of businesses, discover trends, and make well-informed choices. Data gathering, data integration, data modelling, analysis of information, and data visualization are all part of intelligence for businesses.

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Pickl AI

JUNE 28, 2023

Limited Scalability : The process is not workable for handling large volumes of data. ETL (Extract, Transform, Load) ETL is a widely used data integration technique. Pros Automation: ETL tools automate the extraction, transformation, and loading processes. Thereby, improving data quality and consistency.

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Mlearning.ai

MAY 16, 2023

This article discusses five commonly used architectural design patterns in data engineering and their use cases. ETL Design Pattern The ETL (Extract, Transform, Load) design pattern is a commonly used pattern in data engineering. Finally, the transformed data is loaded into the target system.

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phData

SEPTEMBER 26, 2023

Data flows from the current data platform to the destination. Transformations Transformations can be a part of data ingestion (ETL pattern) or can take place at a later stage after data has been landed (ELT pattern). Either way, it’s important to understand what data is transformed, and how so.

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phData

DECEMBER 2, 2024

It allows a developer to perform routine ETL tasks in a low-code/no-code environment using drag-and-drop tools or custom M-Code. This step can be performed as many times as needed across as many data models. If further transformations are needed, those can be done in Power Query.

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Pickl AI

JANUARY 28, 2025

Data Integration Once data is collected from various sources, it needs to be integrated into a cohesive format. Data Quality Management : Ensures that the integrated data is accurate, consistent, and reliable for analysis. They are useful for big data analytics where flexibility is needed.

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Dataconomy

OCTOBER 17, 2023

People who desire to work with big data have to comprehend the architecture of the data warehouse because it helps them understand that they deal with various parts that make up the whole data warehouse. But It’s always better to call data warehouse experts before making a big decision.

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Pickl AI

NOVEMBER 4, 2024

Summary: The fundamentals of Data Engineering encompass essential practices like data modelling, warehousing, pipelines, and integration. Understanding these concepts enables professionals to build robust systems that facilitate effective data management and insightful analysis. What is Data Engineering?

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phData

MAY 11, 2023

With the “Data Productivity Cloud” launch, Matillion has achieved a balance of simplifying source control, collaboration, and dataops by elevating Git integration to a “first-class citizen” within the framework. In Matillion ETL, the Git integration enables an organization to connect to any Git offering (e.g.,

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Women in Big Data

NOVEMBER 27, 2024

By maintaining historical data from disparate locations, a data warehouse creates a foundation for trend analysis and strategic decision-making. Evaluate integration capabilities with existing data sources and Extract Transform and Load (ETL) tools. architecture for both structured and unstructured data.

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Pickl AI

AUGUST 9, 2024

Hierarchies align data modelling with business processes, making it easier to analyse data in a context that reflects real-world operations. Designing Hierarchies Designing effective hierarchies requires careful consideration of the business requirements and the data model.

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ODSC - Open Data Science

SEPTEMBER 27, 2023

In contrast, data warehouses and relational databases adhere to the ‘Schema-on-Write’ model, where data must be structured and conform to predefined schemas before being loaded into the database. Schema Enforcement: Data warehouses use a “schema-on-write” approach.

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Pickl AI

JULY 25, 2023

Data engineers are essential professionals responsible for designing, constructing, and maintaining an organization’s data infrastructure. They create data pipelines, ETL processes, and databases to facilitate smooth data flow and storage. Data Warehousing: Amazon Redshift, Google BigQuery, etc.

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Pickl AI

MARCH 3, 2025

Ensuring data accuracy and consistency through cleansing and validation processes. Data Analysis and Modelling Applying statistical techniques and analytical tools to identify trends, patterns, and anomalies. Developing data models to support analysis and reporting.

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ODSC - Open Data Science

JANUARY 11, 2024

Getting Started with AI in High-Risk Industries, How to Become a Data Engineer, and Query-Driven Data Modeling How To Get Started With Building AI in High-Risk Industries This guide will get you started building AI in your organization with ease, axing unnecessary jargon and fluff, so you can start today.

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phData

AUGUST 10, 2023

Data Vault Automation Working at scale can be challenging, especially when managing the data model. Automations provide extra support to small teams by having templates to automate data integration, data vault modeling, and ETL /DDL code generation. This is where automation tools come into play.

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Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

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Dataconomy

MARCH 24, 2023

Some of the common career opportunities in BI include: Entry-level roles Data analyst:  A data analyst is responsible for collecting and analyzing data, creating reports, and presenting insights to stakeholders. They may also be involved in data modeling and database design.

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phData

JUNE 6, 2024

Composable CDP’s Traditionally, CDPs have been an out-of-the-box solution companies can buy and “plug and play” into their data infrastructure. These traditional CDPs are designed to gather and house their own data store—separate from the core data infrastructure.

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phData

SEPTEMBER 25, 2024

With the importance of data in various applications, there’s a need for effective solutions to organize, manage, and transfer data between systems with minimal complexity. While numerous ETL tools are available on the market, selecting the right one can be challenging.

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ODSC - Open Data Science

APRIL 6, 2023

But its status as the go-between for programming and data professionals isn’t its only power. Within SQL you can also filter data, aggregate it and create valuations, manipulate data, update it, and even do data modeling. Data integration tools allow for the combining of data from multiple sources.

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Pickl AI

JANUARY 15, 2025

They help stakeholders monitor performance at a glance and make timely decisions based on current data. ETL (Extract, Transform, Load) Tools ETL tools are crucial for data integration processes. Companies like Siemens leverage this tool for operational reporting and performance management across their global operations.

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phData

JULY 17, 2023

In order to fully leverage this vast quantity of collected data, companies need a robust and scalable data infrastructure to manage it. This is where Fivetran and the Modern Data Stack come in. Snowflake Data Cloud Replication Transferring data from a source system to a cloud data warehouse.

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phData

JUNE 27, 2023

Having a singular ELT/ETL solution is a firm requirement Dbt does not handle ingestion well, it works best when paired with another ingestion tool such as Fivetran. How to Add dbt to Your Workbooks To get the add-on, you must be your organization’s Admin and can grant access to Creator roles enabling them to view the dbt data.

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SQL Database Data Quality Data Warehouse 52

Alation

OCTOBER 27, 2022

We document these custom models in Alation Data Catalog and publish common queries that other teams can use for operational use cases or reporting needs. Contact title mappings, which are buiilt in some of data models, are documented within our data catalog. Jason: How do you use these models?

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Precisely

MARCH 9, 2023

Processing speeds were considerably slower than they are today, so large volumes of data called for an approach in which data was staged in advance, often running ETL (extract, transform, load) processes overnight to enable next-day visibility to key performance indicators.

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IBM Journey to AI blog

JANUARY 5, 2023

The right data architecture can help your organization improve data quality because it provides the framework that determines how data is collected, transported, stored, secured, used and shared for business intelligence and data science use cases. Perform data quality monitoring based on pre-configured rules.

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