Smart Data Collective

SEPTEMBER 23, 2020

The market for data warehouses is booming. While there is a lot of discussion about the merits of data warehouses, not enough discussion centers around data lakes. We talked about enterprise data warehouses in the past, so let’s contrast them with data lakes. Data Warehouse.

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Smart Data Collective

OCTOBER 17, 2022

Data pipelines automatically fetch information from various disparate sources for further consolidation and transformation into high-performing data storage. There are a number of challenges in data storage , which data pipelines can help address. The movement of data in a pipeline from one point to another.

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Data Pipeline Data Warehouse ETL Exploratory Data Analysis 145

Dataconomy

OCTOBER 8, 2024

Accurate and secure data can help to streamline software engineering processes and lead to the creation of more powerful AI tools, but it has become a challenge to maintain the quality of the expansive volumes of data needed by the most advanced AI models. Featured image credit: Shubham Dhage/Unsplash

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IBM Data Science in Practice

JANUARY 13, 2025

The blog post explains how the Internal Cloud Analytics team leveraged cloud resources like Code-Engine to improve, refine, and scale the data pipelines. Background One of the Analytics teams tasks is to load data from multiple sources and unify it into a data warehouse. Database size limits of 10GB.

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ETL Data Pipeline Database Data Warehouse 100

Smart Data Collective

SEPTEMBER 8, 2021

The ETL process is defined as the movement of data from its source to destination storage (typically a Data Warehouse) for future use in reports and analyzes. The data is initially extracted from a vast array of sources before transforming and converting it to a specific format based on business requirements.

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Smart Data Collective

OCTOBER 27, 2021

Snowflake provides the right balance between the cloud and data warehousing, especially when data warehouses like Teradata and Oracle are becoming too expensive for their users. It is also easy to get started with Snowflake as the typical complexity of data warehouses like Teradata and Oracle are hidden from the users. .

Analytics 130

Analytics Analytics Data Warehouse Business Intelligence 130

Women in Big Data

NOVEMBER 27, 2024

A data warehouse is a centralized repository designed to store and manage vast amounts of structured and semi-structured data from multiple sources, facilitating efficient reporting and analysis. Begin by determining your data volume, variety, and the performance expectations for querying and reporting.

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Pickl AI

JULY 8, 2024

Summary: This blog explains how to build efficient data pipelines, detailing each step from data collection to final delivery. Introduction Data pipelines play a pivotal role in modern data architecture by seamlessly transporting and transforming raw data into valuable insights.

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The MLOps Blog

MAY 17, 2023

We also discuss different types of ETL pipelines for ML use cases and provide real-world examples of their use to help data engineers choose the right one. What is an ETL data pipeline in ML? Xoriant It is common to use ETL data pipeline and data pipeline interchangeably.

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Data Science Connect

JANUARY 27, 2023

Data engineering is a crucial field that plays a vital role in the data pipeline of any organization. It is the process of collecting, storing, managing, and analyzing large amounts of data, and data engineers are responsible for designing and implementing the systems and infrastructure that make this possible.

Data Engineering 130

Data Engineering Data Engineer Data Engineering Data Engineering 130

Smart Data Collective

SEPTEMBER 26, 2021

There’s not much value in holding on to raw data without putting it to good use, yet as the cost of storage continues to decrease, organizations find it useful to collect raw data for additional processing. The raw data can be fed into a database or data warehouse. If it’s not done right away, then later.

Database 130

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phData

JUNE 14, 2023

In this blog, we will explore the benefits of enabling the CI/CD pipeline for database platforms. We will also discuss the difference between imperative and declarative database change management approaches. These environments house the database and schema objects required for both governed and non-governed instances.

Data Pipeline 52

Data Pipeline Database SQL Data Engineer 52

phData

MARCH 8, 2023

Examples of data sources and destinations include: Shopify Google Analytics Snowflake Data Cloud Oracle Salesforce Fivetran’s mission is to, “make access to data as easy as electricity” – so for the last 10 years, they have developed their platform into a leader in the cloud-based ELT market. What is Fivetran Used For?

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Data Warehouse Data Engineer Data Engineering Data Engineering 96

phData

JULY 17, 2023

Over the past few decades, the corporate data landscape has changed significantly. The shift from on-premise databases and spreadsheets to the modern era of cloud data warehouses and AI/ LLMs has transformed what businesses can do with data. This is where Fivetran and the Modern Data Stack come in.

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Women in Big Data

OCTOBER 9, 2024

The success of any data initiative hinges on the robustness and flexibility of its big data pipeline. What is a Data Pipeline? A traditional data pipeline is a structured process that begins with gathering data from various sources and loading it into a data warehouse or data lake.

Big Data 59

Big Data Big Data Apache Kafka Data Pipeline 59

IBM Journey to AI blog

OCTOBER 22, 2024

This adaptability allows organizations to align their data integration efforts with distinct operational needs, enabling them to maximize the value of their data across diverse applications and workflows. This strategy helps organizations optimize data usage, expand into new markets, and increase revenue.

Data Silos 102

Data Silos Data Pipeline DataOps Business Intelligence 102

ODSC - Open Data Science

AUGUST 23, 2023

Modin empowers practitioners to use pandas on data at scale, without requiring them to change a single line of code. Modin leverages our cutting-edge academic research on dataframes — the abstraction underlying pandas to bring the best of databases and distributed systems to dataframes. Run operations in pandas - all in Snowflake!

Data Warehouse 52

Data Warehouse Data Science Database SQL 52

Flipboard

AUGUST 17, 2023

Amazon Redshift is the most popular cloud data warehouse that is used by tens of thousands of customers to analyze exabytes of data every day. AWS Glue is a serverless data integration service that makes it easy to discover, prepare, and combine data for analytics, ML, and application development.

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Analytics Vidhya

JUNE 28, 2022

This article was published as a part of the Data Science Blogathon. “Preponderance data opens doorways to complex and Avant analytics.” ” Introduction to SQL Queries Data is the premium product of the 21st century.

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SQL Data Science Analytics Analytics 270

ODSC - Open Data Science

FEBRUARY 24, 2023

There are many well-known libraries and platforms for data analysis such as Pandas and Tableau, in addition to analytical databases like ClickHouse, MariaDB, Apache Druid, Apache Pinot, Google BigQuery, Amazon RedShift, etc. VisiData works with CSV files, Excel spreadsheets, SQL databases, and many other data sources.

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Exploratory Data Analysis Data Visualization Data Analysis Data Analysis 126

Pickl AI

NOVEMBER 4, 2024

Effective data governance enhances quality and security throughout the data lifecycle. What is Data Engineering? Data Engineering is designing, constructing, and managing systems that enable data collection, storage, and analysis. This section explores essential aspects of Data Engineering.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Analytics Vidhya

FEBRUARY 20, 2023

Introduction Azure data factory (ADF) is a cloud-based data ingestion and ETL (Extract, Transform, Load) tool. The data-driven workflow in ADF orchestrates and automates data movement and data transformation.

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The MLOps Blog

MARCH 15, 2023

In this post, you will learn about the 10 best data pipeline tools, their pros, cons, and pricing. A typical data pipeline involves the following steps or processes through which the data passes before being consumed by a downstream process, such as an ML model training process.

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Data Pipeline ETL SQL Data Quality 59

Pickl AI

AUGUST 1, 2023

The primary goal of Data Engineering is to transform raw data into a structured and usable format that can be easily accessed, analyzed, and interpreted by Data Scientists, analysts, and other stakeholders. Future of Data Engineering The Data Engineering market will expand from $18.2

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ODSC - Open Data Science

JANUARY 30, 2024

So let’s do a quick overview of the job of data engineer, and maybe you might find a new interest. Building and maintaining data pipelines Data integration is the process of combining data from multiple sources into a single, consistent view. Think of data engineers as the architects of the data ecosystem.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

phData

APRIL 29, 2024

Recognizing these specific needs, Fivetran has developed a range of connectors, including dedicated applications, databases, files, and events, which can accommodate the diverse formats used by healthcare systems. Addressing these needs may pose challenges that lead to the implementation of custom solutions rather than a uniform approach.

SQL 52

SQL Data Warehouse Azure Cloud Data 52

phData

SEPTEMBER 2, 2024

Best practices are a pivotal part of any software development, and data engineering is no exception. This ensures the data pipelines we create are robust, durable, and secure, providing the desired data to the organization effectively and consistently. Database names, Cloud Region, etc.

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Mlearning.ai

FEBRUARY 16, 2023

The ultimate need for vast storage spaces manifests in data warehouses: specialized systems that aggregate data coming from numerous sources for centralized management and consistency. In this article, you’ll discover what a Snowflake data warehouse is, its pros and cons, and how to employ it efficiently.

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Data Warehouse Business Intelligence Business Intelligence Database 52

Alation

JANUARY 17, 2023

The modern data stack is a combination of various software tools used to collect, process, and store data on a well-integrated cloud-based data platform. It is known to have benefits in handling data due to its robustness, speed, and scalability. A typical modern data stack consists of the following: A data warehouse.

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Data Warehouse ETL Tableau Cloud Data 52

Pickl AI

OCTOBER 17, 2024

Introduction ETL plays a crucial role in Data Management. This process enables organisations to gather data from various sources, transform it into a usable format, and load it into data warehouses or databases for analysis.

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ODSC - Open Data Science

FEBRUARY 19, 2025

Well according to Brij Kishore Pandey, it stands for Extract, Transform, Load and is a fundamental process in data engineering, ensuring data moves efficiently from raw sources to structured storage for analysis. The stepsinclude: Extraction : Data is collected from multiple sources (databases, APIs, flatfiles).

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phData

FEBRUARY 14, 2023

Data integration is essentially the Extract and Load portion of the Extract, Load, and Transform (ELT) process. Data ingestion involves connecting your data sources, including databases, flat files, streaming data, etc, to your data warehouse. Snowflake provides native ways for data ingestion.

Data Warehouse 59

Data Warehouse Azure AWS Database 59

IBM Journey to AI blog

SEPTEMBER 19, 2023

And you should have experience working with big data platforms such as Hadoop or Apache Spark. Additionally, data science requires experience in SQL database coding and an ability to work with unstructured data of various types, such as video, audio, pictures and text.

Data Science 94

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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.

Data Engineering 52

Data Engineering Data Engineer Data Engineering Data Engineering 52

Pickl AI

JULY 25, 2024

Must Read Blogs: Exploring the Power of Data Warehouse Functionality. Data Lakes Vs. Data Warehouse: Its significance and relevance in the data world. Exploring Differences: Database vs Data Warehouse. It is commonly used in data warehouses for business analytics and reporting.

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Data Warehouse Business Intelligence Business Intelligence Database 52

phData

SEPTEMBER 15, 2023

Salesforce Sync Out is a crucial tool that enables businesses to transfer data from their Salesforce platform to external systems like Snowflake, AWS S3, and Azure ADLS. Warehouse for loading the data (start with XSMALL or SMALL warehouses).

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phData

JANUARY 5, 2023

On-Premises to The Cloud This type of migration involves moving an organization’s BI platform from an on-premises environment (such as a local server or data center) to a cloud-based environment. For example, suppose an organization moves from an on-premises database to a cloud-based database like Snowflake.

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