Workfall

AUGUST 14, 2023

Reading Time: 7 minutes In today’s data-driven world, efficient data pipelines have become the backbone of successful organizations. These pipelines ensure that data flows smoothly from various sources to its intended destinations, enabling businesses to make informed decisions and gain valuable insights.

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Monte Carlo

APRIL 24, 2023

Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform. There are two primary types of raw data. And data orchestration tools are generally easy to stand-up for initial use-cases. Missed Nishith’s 5 considerations?

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ProjectPro

DECEMBER 7, 2021

Data pipelines are a significant part of the big data domain, and every professional working or willing to work in this field must have extensive knowledge of them. Table of Contents What is a Data Pipeline? The Importance of a Data Pipeline What is an ETL Data Pipeline?

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Grouparoo

OCTOBER 26, 2021

As a result, data has to be moved between the source and destination systems and this is usually done with the aid of data pipelines. What is a Data Pipeline? A data pipeline is a set of processes that enable the movement and transformation of data from different sources to destinations.

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DataKitchen

JULY 27, 2023

The Ten Standard Tools To Develop Data Pipelines In Microsoft Azure. While working in Azure with our customers, we have noticed several standard Azure tools people use to develop data pipelines and ETL or ELT processes. We counted ten ‘standard’ ways to transform and set up batch data pipelines in Microsoft Azure.

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Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? Why is data pipeline architecture important?

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DareData

JULY 29, 2020

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines. What makes a good Data Pipeline?

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Ascend.io

APRIL 12, 2023

From this research, we developed a framework with a sequence of stages to implement data integrity quickly and measurably via data pipelines. Table of Contents Why does data integrity matter? At every level of a business, individuals must trust the data, so they can confidently make timely decisions. Let’s explore!

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Hevo

FEBRUARY 15, 2023

After the hustle and bustle of extracting data from multiple sources, you have finally loaded all your data to a single source of truth like the Snowflake data warehouse. However, data modeling is still challenging and critical for transforming your raw data into any analysis-ready form to get insights.

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Dataquest

NOVEMBER 4, 2019

If you’ve ever wanted to learn Python online with streaming data, or data that changes quickly, you may be familiar with the concept of a data pipeline. Data pipelines allow you transform data from one representation to another through a series of steps. We store the raw log data to a database.

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DataKitchen

NOVEMBER 5, 2024

It sounds great, but how do you prove the data is correct at each layer? How do you ensure data quality in every layer ? Bronze, Silver, and Gold – The Data Architecture Olympics? The Bronze layer is the initial landing zone for all incoming raw data, capturing it in its unprocessed, original form.

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Ascend.io

OCTOBER 28, 2024

What is Data Transformation? Data transformation is the process of converting raw data into a usable format to generate insights. It involves cleaning, normalizing, validating, and enriching data, ensuring that it is consistent and ready for analysis.

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Towards Data Science

FEBRUARY 6, 2024

As you do not want to start your development with uncertainty, you decide to go for the operational raw data directly. Accessing Operational Data I used to connect to views in transactional databases or APIs offered by operational systems to request the raw data. Does it sound familiar?

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Netflix Tech

NOVEMBER 14, 2023

We’ll discuss batch data processing, the limitations we faced, and how Psyberg emerged as a solution. Furthermore, we’ll delve into the inner workings of Psyberg, its unique features, and how it integrates into our data pipelining workflows. The fact tables then feed downstream intraday pipelines that process the data hourly.

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Data Engineering Data Engineer Engineering Metadata 107

Ascend.io

SEPTEMBER 16, 2024

How to Design a Data Flow in 6 Steps: 1. Identify Data Sources 3. Define Data Requirements 4. Implement Data Flow Diagrams (DFDs) 5. Build & Optimize Data Pipelines 6. But what are data flows? In this post, we’ll explore how to design, build, and scale your data flows.

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Cloudyard

JUNE 11, 2024

Ensuring the reliability and resilience of these workflows is critical, especially when dealing with production data pipelines. Imagine you’re tasked with managing a critical data pipeline in Snowflake that processes and transforms large datasets. Task B: Transforms the data in the staging table.

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Monte Carlo

AUGUST 6, 2024

Integration Layer : Where your data transformations and business logic are applied. Stage Layer: The Foundation The Stage Layer serves as the foundation of a data warehouse. Its primary purpose is to ingest and store raw data with minimal modifications, preserving the original format and content of incoming data.

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Snowflake

JUNE 6, 2024

Faster, easier AI/ML and data engineering workflows Explore, analyze and visualize data using Python and SQL. Discover valuable business insights through exploratory data analysis. Develop scalable data pipelines and transformations for data engineering.

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SQL Python Machine Learning Data Workflow 95

Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. Their task is straightforward: take the raw data and transform it into a structured, coherent format.

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Snowflake

MAY 23, 2024

Right now we’re focused on raw data quality and accuracy because it’s an issue at every organization and so important for any kind of analytics or day-to-day business operation that relies on data — and it’s especially critical to the accuracy of AI solutions, even though it’s often overlooked.

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ThoughtSpot

MARCH 5, 2024

Managing complex data pipelines is a major challenge for data-driven organizations looking to accelerate analytics initiatives. When created, Snowflake materializes query results into a persistent table structure that refreshes whenever underlying data changes. Now, that’s changing.

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DareData

JULY 5, 2023

If you work at a relatively large company, you've seen this cycle happening many times: Analytics team wants to use unstructured data on their models or analysis. For example, an industrial analytics team wants to use the logs from raw data. If you need help to understand how these tools work, feel free to drop us a message!

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Databand.ai

JULY 19, 2023

The Transform Phase During this phase, the data is prepared for analysis. This preparation can involve various operations such as cleaning, filtering, aggregating, and summarizing the data. The goal of the transformation is to convert the raw data into a format that’s easy to analyze and interpret.

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WeCloudData

OCTOBER 19, 2021

The client needed to build its own internal data pipeline with enough flexibility to meet the business requirements for a job market analysis platform & dashboard. The client intends to build on and improve this data pipeline by moving towards a more serverless architecture and adding DevOps tools & workflows.

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WeCloudData

OCTOBER 19, 2021

The client needed to build its own internal data pipeline with enough flexibility to meet the business requirements for a job market analysis platform & dashboard. The client intends to build on and improve this data pipeline by moving towards a more serverless architecture and adding DevOps tools & workflows.

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Workfall

SEPTEMBER 18, 2023

It’s designed to be user-friendly, customizable, and extensible, making it a valuable tool for data engineers, analysts, and data-driven organizations looking to streamline their data pipelines. With its drag-and-drop interface, creating data pipelines becomes as easy as arranging blocks in a puzzle.

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ThoughtSpot

SEPTEMBER 6, 2023

Those coveted insights live at the end of a process lovingly known as the data pipeline. The pathway from ETL to actionable analytics can often feel disconnected and cumbersome, leading to frustration for data teams and long wait times for business users. Keep reading to see how it works.

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Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

Cloud Data Ingestion Raw Data Data Pipeline 52

Ascend.io

FEBRUARY 6, 2023

Data Flow – is an individual data pipeline. Data Flows include the ingestion of raw data, transformation via SQL and python, and sharing of finished data products. Data Plane – is the data cloud where the data pipeline workload runs, like Databricks, BigQuery, and Snowflake.

Cloud 52

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DataKitchen

AUGUST 9, 2021

The data industry has a wide variety of approaches and philosophies for managing data: Inman data factory, Kimball methodology, s tar schema , or the data vault pattern, which can be a great way to store and organize raw data, and more. Data mesh does not replace or require any of these.

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Grouparoo

DECEMBER 14, 2021

ETL, or Extract, Transform, Load, is a process that involves extracting data from different data sources , transforming it into more suitable formats for processing and analytics, and loading it into the target system, usually a data warehouse. ETL processes are used by organizations to generate business insights from raw data.

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Ascend.io

NOVEMBER 21, 2023

More importantly, we will contextualize ELT in the current scenario, where data is perpetually in motion, and the boundaries of innovation are constantly being redrawn. Extract The initial stage of the ELT process is the extraction of data from various source systems. What Is ELT? So, what exactly is ELT?

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Raw Data Data Warehouse Data Cleanse Data Integration 52

Ascend.io

AUGUST 16, 2023

August 16, 2023 — Ascend.io , the leader in data pipeline automation, today released an economic analysis report conducted by Enterprise Strategy Group (ESG) of its Data Pipeline Automation Platform. As enterprise usage of data analytics grows, the field has become a significant area of IT expenditure.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Pipeline 52

Meltano

OCTOBER 5, 2022

DataOps, which is based on Agile methodology and DevOps best practices, is focused on automating data flow across an organization and the entire data lifecycle, from aggregation to reporting. The goal of DataOps is to speed up the process of deriving value from data. Using automation to streamline data processing.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

Databand.ai

AUGUST 30, 2023

Data testing tools: Key capabilities you should know Helen Soloveichik August 30, 2023 Data testing tools are software applications designed to assist data engineers and other professionals in validating, analyzing and maintaining data quality. There are several types of data testing tools.

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Meltano

OCTOBER 5, 2022

What Is Data Engineering? Data engineering is the process of designing systems for collecting, storing, and analyzing large volumes of data. Put simply, it is the process of making raw data usable and accessible to data scientists, business analysts, and other team members who rely on data.

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Data Engineering Data Engineer Engineering Raw Data 52

Striim

NOVEMBER 17, 2023

Striim is the backbone of this integration, and having a working Striim instance is essential for setting up the data pipelines and connecting to your source databases. You should understand how to navigate the Striim environment, configure data sources, and set up data flows.

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