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

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.

Architecture 79

Architecture Raw Data Pipeline-centric Data Ingestion 79

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

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?

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

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.

Raw Data 52

Raw Data Datasets Aggregated Data Data Pipeline 52

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.

SQL 97

SQL Python Machine Learning Data Workflow 97

Ascend.io

AUGUST 15, 2023

You require a comprehensive solution that addresses every facet, from ingestion and transformation to orchestration and reverse ETL. It’s no surprise, then, that the quest for Fivetran alternatives is on the rise as organizations set their sights on a more holistic data approach. Moreover, raw data often requires refinement.

Data Management 52

Data Management Management Data Ingestion 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

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

Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

DataKitchen

MAY 10, 2024

The Third of Five Use Cases in Data Observability Data Evaluation: This involves evaluating and cleansing new datasets before being added to production. This process is critical as it ensures data quality from the onset. Examples include regular loading of CRM data and anomaly detection.

Raw Data 50

Raw Data Data Ingestion Datasets Data 50

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. The Data Warehouse(s) facilitates data ingestion and enables easy access for end-users.

Data Lake 52

Data Lake Building Raw Data ETL Tools 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

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 Engineer 52

Data Engineer Data Engineering Engineering Pipeline-centric 52

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

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

Ascend.io

FEBRUARY 23, 2024

For this reason, your data platform becomes the foundation for your AI initiatives. Robust Data Ingestion AI systems thrive on diverse data sources. Your platform should be equipped with robust mechanisms for data ingestion and integration, enabling seamless flow of data from various sources into the system.

Cloud Storage 52

Cloud Storage Data Ingestion Machine Learning Algorithm 52

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

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

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 69

Raw Data Data Business Intelligence Data Engineer 69

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

The Modern Data Company

JULY 29, 2024

Already operating at capacity, data teams often find themselves repeating efforts, rebuilding similar data pipelines and models for each new project. The consequences of these challenges are stark: the journey from raw data to actionable insights has become excruciatingly long.

Data Architecture 52

Data Architecture Architecture Building Pipeline-centric 52

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 81

Data Lake SQL Data Data Warehouse 81

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

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

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

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

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

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 73

SQL Data Raw Data Architecture 73

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

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 Engineer 52

Data Engineer Data Engineering Coding Project 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 Engineer 52

Data Engineer Data Engineering Engineering Amazon Web Services 52

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

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