Smart Data Collective

OCTOBER 27, 2021

Interactive analytics applications make it easy to get and build reports from large unstructured data sets fast and at scale. In this article, we’re going to look at the top 5. Firebolt makes engineering a sub-second analytics experience possible by delivering production-grade data applications & analytics. Google BigQuery.

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The MLOps Blog

MAY 17, 2023

Often the Data Team, comprising Data and ML Engineers , needs to build this infrastructure, and this experience can be painful. However, efficient use of ETL pipelines in ML can help make their life much easier. What is an ETL data pipeline in ML? Data pipelines often run real-time processing.

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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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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

phData

JUNE 14, 2023

which play a crucial role in building end-to-end data pipelines, to be included in your CI/CD pipelines. Declarative Database Change Management Approaches For insights into database change management tool selection for Snowflake, check out this article.

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ODSC - Open Data Science

AUGUST 23, 2023

Run pandas at scale on your data warehouse Most enterprise data teams store their data in a database or data warehouse, such as Snowflake, BigQuery, or DuckDB. Ponder solves this problem by translating your pandas code to SQL that can be understood by your data warehouse.

Data Warehouse 52

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Pickl AI

NOVEMBER 4, 2024

The global Big Data and Data Engineering Services market, valued at USD 51,761.6 This article explores the key fundamentals of Data Engineering, highlighting its significance and providing a roadmap for professionals seeking to excel in this vital field. What is Data Engineering? million by 2028.

Data Engineering 52

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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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ODSC - Open Data Science

FEBRUARY 24, 2023

While machine learning frameworks and platforms like PyTorch, TensorFlow, and scikit-learn can perform data exploration well, it’s not their primary intent. There are also plenty of data visualization libraries available that can handle exploration like Plotly, matplotlib, D3, Apache ECharts, Bokeh, etc.

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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

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Alation

JANUARY 17, 2023

In this article, I will explain the modern data stack in detail, list some benefits, and discuss what the future holds. What Is the Modern Data Stack? 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.

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Pickl AI

OCTOBER 17, 2024

Summary: This article explores the significance of ETL Data in Data Management. It highlights key components of the ETL process, best practices for efficiency, and future trends like AI integration and real-time processing, ensuring organisations can leverage their data effectively for strategic decision-making.

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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

Dataconomy

FEBRUARY 23, 2023

Collecting, storing, and processing large datasets Data engineers are also responsible for collecting, storing, and processing large volumes of data. This involves working with various data storage technologies, such as databases and data warehouses, and ensuring that the data is easily accessible and can be analyzed efficiently.

Big Data 91

Big Data Big Data Data Engineering Data Engineering 91

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

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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. Read more to know. Cloud Platforms: AWS, Azure, Google Cloud, etc.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

OCTOBER 17, 2024

It involves extracting data from various sources, transforming it into a suitable format, and loading it into a target system for analysis and reporting. As organisations increasingly rely on data-driven insights, effective ETL processes ensure data integrity and quality, enabling informed decision-making.

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ODSC - Open Data Science

FEBRUARY 6, 2024

This open-source streaming platform enables the handling of high-throughput data feeds, ensuring that data pipelines are efficient, reliable, and capable of handling massive volumes of data in real-time. Its open-source nature means it’s continually evolving, thanks to contributions from its user community.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

ODSC - Open Data Science

JANUARY 18, 2024

This individual is responsible for building and maintaining the infrastructure that stores and processes data; the kinds of data can be diverse, but most commonly it will be structured and unstructured data. They’ll also work with software engineers to ensure that the data infrastructure is scalable and reliable.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

IBM Journey to AI blog

JULY 17, 2023

sales conversation summaries, insurance coverage, meeting transcripts, contract information) Generate: Generate text content for a specific purpose, such as marketing campaigns, job descriptions, blogs or articles, and email drafting support. The post Exploring the AI and data capabilities of watsonx appeared first on IBM Blog.

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AI AI Machine Learning Machine Learning 74

The MLOps Blog

JANUARY 26, 2024

For this, we have to build an entire machine-learning system around our models that manages their lifecycle, feeds properly prepared data into them, and sends their output to downstream systems. An ML system needs to transform the data into features, train models, and make predictions. This can seem daunting.

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Machine Learning Machine Learning ML ML 72

ODSC - Open Data Science

APRIL 4, 2024

Find out how to weave data reliability and quality checks into the execution of your data pipelines and more. More Speakers and Sessions Announced for the 2024 Data Engineering Summit Ranging from experimentation platforms to enhanced ETL models and more, here are some more sessions coming to the 2024 Data Engineering Summit.

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Data Visualization Analytics Analytics Big Data Analytics 52

ODSC - Open Data Science

FEBRUARY 15, 2024

These systems represent data as knowledge graphs and implement graph traversal algorithms to help find content in massive datasets. These systems are not only useful for a wide range of industries, they are fun for data engineers to work on. So get your pass today, and keep yourself ahead of the curve.

Data Engineering 52

Data Engineering Data Engineering Data Engineer Data Engineering 52

Pickl AI

DECEMBER 9, 2024

Summary: This article provides a comprehensive guide on Big Data interview questions, covering beginner to advanced topics. Introduction Big Data continues transforming industries, making it a vital asset in 2025. The global Big Data Analytics market, valued at $307.51 billion in 2023, is projected to grow to $348.21

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Dataversity

APRIL 30, 2021

The goal of digital transformation remains the same as ever – to become more data-driven. We have learned how to gain a competitive advantage by capturing business events in data. Events are data snap-shots of complex activity sourced from the web, customer systems, ERP transactions, social media, […].

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Dataversity

SEPTEMBER 27, 2021

DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. Click to learn more about author Wayne Yaddow. ETL projects are increasingly based on agile processes and automated testing.

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Pickl AI

OCTOBER 15, 2024

The right ETL platform ensures data flows seamlessly across systems, providing accurate and consistent information for decision-making. Effective integration is crucial to maintaining operational efficiency and data accuracy, as modern businesses handle vast amounts of data. What is ETL in Data Integration?

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ETL Azure AWS Data Governance 52

Alation

JANUARY 26, 2023

That’s why many organizations invest in technology to improve data processes, such as a machine learning data pipeline. However, data needs to be easily accessible, usable, and secure to be useful — yet the opposite is too often the case. These data requirements could be satisfied with a strong data governance strategy.

Data Governance 52

Data Governance Data Engineering Data Engineering Data Engineer 52

The MLOps Blog

JANUARY 23, 2023

As data is the foundation of any machine learning project, it is essential to have a system in place for tracking and managing changes to data over time. However, data versioning control is frequently given little attention, leading to issues such as data inconsistencies and the inability to reproduce results.

ML 52

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phData

OCTOBER 20, 2023

Speed: The agent on the source database will filter the data before sending it through the data pipeline. That’s why filtering close to the source improves the efficiency and increases the speed but only copying data, which is required. HVA also allows the capture of changes directly from various DBMS articles.

Database 52

Database SQL ETL Data Warehouse 52

DagsHub

AUGUST 23, 2024

Image generated with Midjourney Organizations increasingly rely on data to make business decisions, develop strategies, or even make data or machine learning models their key product. As such, the quality of their data can make or break the success of the company. What is a data quality framework?

Data Quality 52

Data Quality Data Governance Machine Learning Machine Learning 52

Mlearning.ai

JULY 10, 2023

Click here for link to Part 1 of this article Continuing the Beginner’s Guide to GCP BigQuery series; in Part 2, we will take a look at the advantages and use cases of key features in BigQuery. This allows you to use tools like BigQuery to query the data before it’s migrated to a native BigQuery table.

SQL 52

SQL Database Database Administration Data Lakes 52

Dataversity

APRIL 1, 2021

In the data-driven world we live in today, the field of analytics has become increasingly important to remain competitive in business. In fact, a study by McKinsey Global Institute shows that data-driven organizations are 23 times more likely to outperform competitors in customer acquisition and nine times […].

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Analytics Analytics Data Silos Data Lakes 69

The MLOps Blog

MARCH 15, 2023

This article is a real-life study of building a CI/CD MLOps pipeline. ETL usually stands for “Extract, Transform and Load,” and it refers to a process in data warehousing. Sourcing the data In our case, the data was provided by our client, which was a product-based organization.

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Mlearning.ai

MARCH 15, 2023

Introduction Welcome Back, Let's continue with our Data Science journey to create the Stock Price Prediction web application. This is part 2 of the three-series article. If you are here for the first time then please check out this article first. I think it’s enough visualization for the project.

Python 52

Python AWS Exploratory Data Analysis Machine Learning 52

Dataversity

AUGUST 30, 2021

DataOps, which focuses on automated tools throughout the ETL development cycle, responds to a huge challenge for data integration and ETL projects in general. Click to learn more about author Wayne Yaddow. ETL projects are increasingly based on agile processes and automated testing.

DataOps 52

DataOps ETL Data Pipeline Data Warehouse 52

AWS Machine Learning Blog

OCTOBER 24, 2024

Data pipelines must seamlessly integrate new data at scale. Diverse data amplifies the need for customizable cleaning and transformation logic to handle the quirks of different sources. You can build and manage an incremental data pipeline to update embeddings on Vectorstore at scale.

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