Ascend.io

JANUARY 10, 2024

This continuous adaptation ensures that your data management stays effective and compliant with current standards. The goal is to ensure your organization has the capability to process and prepare data effectively for your AI models. Your data pipeline platform should excel in collecting data from a wide array of sources.

Data Pipeline 52

Data Pipeline Government Data Governance Raw Data 52

Knowledge Hut

NOVEMBER 2, 2023

An Azure Data Engineer is a professional responsible for designing, implementing, and managing data solutions using Microsoft's Azure cloud platform. They work with various Azure services and tools to build scalable, efficient, and reliable data pipelines, data storage solutions, and data processing systems.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Data Engineering Podcast

JUNE 26, 2022

Summary The most complicated part of data engineering is the effort involved in making the raw data fit into the narrative of the business. Your newly mimicked datasets are safe to share with developers, QA, data scientists—heck, even distributed teams around the world. In fact, while only 3.5% In fact, while only 3.5%

Data Management 100

Data Management Management MongoDB Scala 100

Cloudera

JANUARY 20, 2021

We have simplified this journey into five discrete steps with a common sixth step speaking to data security and governance. The six steps are: Data Collection – data ingestion and monitoring at the edge (whether the edge be industrial sensors or people in a brick and mortar retail store). Conclusion.

Manufacturing 108

Manufacturing Data Warehouse Kafka Retail 108

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.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

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.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

AltexSoft

AUGUST 29, 2023

The term was coined by James Dixon , Back-End Java, Data, and Business Intelligence Engineer, and it started a new era in how organizations could store, manage, and analyze their data. This article explains what a data lake is, its architecture, and diverse use cases. Video explaining how data streaming works.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

DataKitchen

OCTOBER 2, 2023

Data Teams and Their Types of Data Journeys In the rapidly evolving landscape of data management and analytics, data teams face various challenges ranging from data ingestion to end-to-end observability. It explores why DataKitchen’s ‘Data Journeys’ capability can solve these challenges.

Data Ingestion 40

Data Ingestion Data Government Datasets 40

Towards Data Science

MAY 3, 2023

One such tool is the Versatile Data Kit (VDK), which offers a comprehensive solution for controlling your data versioning needs. VDK helps you easily perform complex operations, such as data ingestion and processing from different sources, using SQL or Python. Join the #versatile-data-kit channel.

Data Lake 87

Data Lake Data SQL Data Warehouse 87

ProjectPro

FEBRUARY 25, 2022

Efficient Scheduling and Runtime Increased Adaptability and Scope Faster Analysis and Real-Time Prediction Introduction to the Machine Learning Pipeline Architecture How to Build an End-to-End a Machine Learning Pipeline? Is python suitable for machine learning pipeline design patterns?

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52

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. Data ingestion When we think about the flow of data in a pipeline, data ingestion is where the data first enters our platform.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Rockset

AUGUST 30, 2021

All data will be indexed in real-time , and Rockset’s distributed SQL engine will leverage the indexes and provide sub-second query response times. But until this release, all these data sources involved indexing the incoming raw data on a record by record basis. That is sufficient for some use cases.

SQL 52

SQL Kafka MongoDB MySQL 52

Monte Carlo

MARCH 14, 2023

Data ingestion is the process of collecting data from various sources and moving it to your data warehouse or lake for processing and analysis. It is the first step in modern data management workflows. Table of Contents What is Data Ingestion? Decision making would be slower and less accurate.

Data Ingestion 52

Data Ingestion Data Warehouse Lambda Architecture Raw Data 52

ProjectPro

JANUARY 19, 2022

The first step is to work on cleaning it and eliminating the unwanted information in the dataset so that data analysts and data scientists can use it for analysis. That needs to be done because raw data is painful to read and work with.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Ascend.io

JULY 28, 2023

Data pipelines are integral to business operations, regardless of whether they are meticulously built in-house or assembled using various tools. As companies become more data-driven, the scope and complexity of data pipelines inevitably expand. Ready to fortify your data management practice?

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

Ascend.io

JANUARY 3, 2023

However, most data leaders are finding that technology alone does not cause the organization to deliver new and valuable insights fast enough. Fundamentally, we need an approach that holistically supports the infrastructure, technology, and processes to convert raw data into something valuable and accessible.

Data 52

Data Data Lake Business Intelligence Big Data 52

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?

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Ascend.io

AUGUST 29, 2023

A star-studded baseball team is analogous to an optimized “end-to-end data pipeline” — both require strategy, precision, and skill to achieve success. Just as every play and position in baseball is key to a win, each component of a data pipeline is integral to effective data management.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 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

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

Cloud Data Ingestion Raw Data Data Pipeline 52

DataKitchen

SEPTEMBER 21, 2023

In the contemporary data landscape, data teams commonly utilize data warehouses or lakes to arrange their data into L1, L2, and L3 layers. The current landscape of Data Observability Tools shows a marked focus on “Data in Place,” leaving a significant gap in the “Data in Use.”

Raw Data 70

Raw Data Data Business Intelligence High Quality Data 70

Snowflake

MARCH 13, 2023

Architecture designed to empower more clients Gem’s cybersecurity platform starts with raw data ingestion from its clients’ cloud environments. Gem uses the fully-managed Snowpipe service, allowing it to stream and process source data in near-real time. Pushing and scaling are super smooth.

Cloud 52

Cloud Building Data Ingestion AWS 52

Monte Carlo

NOVEMBER 14, 2023

Data incompleteness: Corrupted, incomplete, or data missing from your tables, such as data ingested without all the required fields or data damaged due to human or technical errors.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

AltexSoft

JUNE 26, 2023

Data collection vs data integration vs data ingestion Data collection is often confused with data ingestion and data integration — other important processes within the data management strategy. While all three are about data acquisition, they have distinct differences.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

Snowflake

SEPTEMBER 11, 2023

The intention of Dynamic Tables is to apply incremental transformations on near real-time data ingestion that Snowflake now supports with Snowpipe Streaming. Data enters Snowflake in its raw operational form (event data) and Dynamic Tables transforms that raw data into a form that serves analytical value.

SQL 71

SQL Data Raw Data Architecture 71

ProjectPro

AUGUST 24, 2021

Within no time, most of them are either data scientists already or have set a clear goal to become one. Nevertheless, that is not the only job in the data world. And, out of these professions, this blog will discuss the data engineering job role. This big data project discusses IoT architecture with a sample use case.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Monte Carlo

FEBRUARY 15, 2023

Cleaning Bad data can derail an entire company, and the foundation of bad data is unclean data. Therefore it’s of immense importance that the data that enters a data warehouse needs to be cleaned. Finally, where and how the data pipeline broke isn’t always obvious. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

U-Next

JUNE 29, 2022

Data must be consumed from many sources, translated and stored, and then processed before being presented understandably. However, the benefits might be game-changing: a well-designed big data pipeline can significantly differentiate a company. Data ingestion can be divided into two categories: .

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

Knowledge Hut

JANUARY 18, 2024

Let us now look into the differences between AI and Data Science: Data Science vs Artificial Intelligence [Comparison Table] SI Parameters Data Science Artificial Intelligence 1 Basics Involves processes such as data ingestion, analysis, visualization, and communication of insights derived.

Data Science 98

Data Science Deep Learning Business Analyst Data Mining 98

Zalando Engineering

MARCH 21, 2017

Instead, we can focus on building a flexible and versatile model that can be easily extended to new types of input data and applied to a variety of prediction tasks. In general, learning from raw data can help to avoid limitations when placing too much confidence in human domain modeling.

Deep Learning 52

Deep Learning Raw Data Machine Learning AWS 52

ProjectPro

JANUARY 31, 2023

Big data operations require specialized tools and techniques since a relational database cannot manage such a large amount of data. Big data enables businesses to gain a deeper understanding of their industry and helps them extract valuable information from the unstructured and raw data that is regularly collected.

Big Data 52

Big Data Hadoop AWS Relational Database 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

AltexSoft

MAY 14, 2021

Big Data analytics encompasses the processes of collecting, processing, filtering/cleansing, and analyzing extensive datasets so that organizations can use them to develop, grow, and produce better products. Big Data analytics processes and tools. Data ingestion. Let’s take a closer look at these procedures.

Big Data 78

Big Data Data Analytics IT NoSQL 78

Monte Carlo

MARCH 28, 2023

The most prominent, recent examples are Snowflake and Databricks disrupting the concept of the database and ushering in the modern data stack era. As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Zero-ETL What it is : A misnomer for one thing; the data pipeline still exists.

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Towards Data Science

APRIL 3, 2023

The most prominent, recent examples are Snowflake and Databricks disrupting the concept of the database and ushering in the modern data stack era. As part of this movement, Fivetran and dbt fundamentally altered the data pipeline from ETL to ELT. Zero-ETL What it is : A misnomer for one thing; the data pipeline still exists.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

Confluent

OCTOBER 16, 2019

There’s also some static reference data that is published on web pages. ?After Wrangling the data. With the raw data in Kafka, we can now start to process it. Since we’re using Kafka, we are working on streams of data. After we scrape these manually, they are produced directly into a Kafka topic.

Kafka 19

Kafka Building Data Coding 19

U-Next

SEPTEMBER 23, 2022

In order to comprehend what are the main components of Big Data , one must understand the basic l ayers of Big Data components of database stack together to form a stack. It is not as simple as converting data into insights. In this post, we’ll explain each Big Data component along with the Big Data ecosystem.

Big Data Ecosystem 40

Big Data Ecosystem Big Data Healthcare Data Lake 40