Ascend.io

FEBRUARY 23, 2024

Before we explore the specific requirements your AI data platform, let’s evaluate your technical foundation’s readiness for AI. Critical considerations include: Do you have the cloud capabilities necessary to scale with AI’s demands? Is your data environment diverse and accessible enough to fuel AI algorithms?

Cloud Storage 52

Cloud Storage Data Ingestion Machine Learning Algorithm 52

Monte Carlo

APRIL 24, 2023

In an evolving data landscape, the explosion of new tooling solutions—from cloud-based transforms to data observability —has made the question of “build versus buy” increasingly important for data leaders. There are two primary types of raw data. Still need a little more refreshing before you move on?

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Knowledge Hut

DECEMBER 7, 2023

Welcome to the comprehensive guide for beginners on harnessing the power of Microsoft's remarkable data visualization tool - Power BI. In today's data-driven world, the ability to transform raw data into meaningful insights is paramount, and Power BI empowers users to achieve just that. What is Power BI?

BI 52

BI Raw Data Datasets Business Intelligence 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

Cloudera

JANUARY 20, 2021

Most of what is written though has to do with the enabling technology platforms (cloud or edge or point solutions like data warehouses) or use cases that are driving these benefits (predictive analytics applied to preventive maintenance, financial institution’s fraud detection, or predictive health monitoring as examples) not the underlying data.

Manufacturing 103

Manufacturing Data Warehouse Kafka Retail 103

Ascend.io

JANUARY 2, 2024

The key differentiation lies in the transformational steps that a data pipeline includes to make data business-ready. Ultimately, the core function of a pipeline is to take raw data and turn it into valuable, accessible insights that drive business growth. best suit our processed data? cleaning, formatting)?

Data Pipeline 52

Data Pipeline Building Raw Data Data Warehouse 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

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. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool.

Data Management 100

Data Management Management MongoDB Scala 100

Knowledge Hut

NOVEMBER 2, 2023

This article will discuss the differences between the Azure data engineer vs Azure devops engineer job titles in order to give you some valuable insights into these careers to provide clarity regarding which one would be a better fit for you. Who is an Azure Data Engineer?

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Monte Carlo

MARCH 28, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Warehouse 126

Data Warehouse Raw Data Data Pipeline Software Engineer 126

Ascend.io

AUGUST 29, 2023

Similarly , in data, every step of the pipeline, from data ingestion to delivery, plays a pivotal role in delivering impactful results. In this article, we’ll break down the intricacies of an end-to-end data pipeline and highlight its importance in today’s landscape.

Data Pipeline 52

Data Pipeline Pipeline-centric Database-centric Data Ingestion 52

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value. ML workflow, ubr.to/3EJHjvm

Engineering 79

Engineering Raw Data Data Science Scala 79

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

Monte Carlo

AUGUST 19, 2023

We’ll cover: What is a data platform? It becomes especially important to have a data storage and processing layer when you start to deal with large amounts of data and are holding that data for a long period of time and need it to be readily available for analysis.

Building 52

Building BI Data Lake Data Governance 52

Monte Carlo

APRIL 24, 2023

By accommodating various data types, reducing preprocessing overhead, and offering scalability, data lakes have become an essential component of modern data platforms , particularly those serving streaming or machine learning use cases.

Data Lake 52

Data Lake Google Cloud Data Warehouse AWS 52

Snowflake

MARCH 27, 2023

It seems everyone has a handful of such shapes in their raw data, and in the past they had to fix those shapes outside of Snowflake before ingesting them. Introducing CARTO Workflows Snowflake’s powerful data ingestion and transformation features help many data engineers and analysts who prefer SQL.

Generalist 52

Generalist Raw Data Business Analyst SQL 52

Towards Data Science

APRIL 3, 2023

And so it almost seems unfair that new ideas are already springing up to disrupt the disruptors: Zero-ETL has data ingestion in its sights AI and Large Language Models could transform transformation Data product containers are eyeing the table’s thrown as the core building block of data Are we going to have to rebuild everything (again)?

Data Engineering 98

Data Engineering Data Engineer Engineering Data Warehouse 98

Monte Carlo

JUNE 14, 2023

Why is data pipeline architecture important? Data pipeline architecture typically consisted of hardcoded pipelines that cleaned, normalized, and transformed the data prior to loading into a database using an ETL pattern. Data could now be extracted and loaded prior to being transformed for its ultimate use.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

FEBRUARY 15, 2023

Since the inception of the cloud, there has been a massive push to store any and all data. On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 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

WeCloudData

OCTOBER 19, 2021

By leveraging data engineering techniques combined with a cloud toolchain, WeCloudData helped a client achieve a continuous flow of current job market data with analytical capabilities and dashboards to drive the business forward and stay competitive.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

WeCloudData

OCTOBER 19, 2021

By leveraging data engineering techniques combined with a cloud toolchain, WeCloudData helped a client achieve a continuous flow of current job market data with analytical capabilities and dashboards to drive the business forward and stay competitive.

Consulting 52

Consulting Raw Data Data Lake Data Pipeline 52

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

APRIL 25, 2024

Generally, five key steps comprise the standard workflow for spatial data scientists, which takes them from data collection to offering business insights after the process. Data engineering and the required ETL workflow usually come first in a pipeline for data science.

Data Science 52

Data Science Telecommunication Transportation Big Data 52

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data.

Scala 64

Scala Data Lake BI Google Cloud 64

AltexSoft

MAY 12, 2023

Tools and platforms for unstructured data management Unstructured data collection Unstructured data collection presents unique challenges due to the information’s sheer volume, variety, and complexity. The process requires extracting data from diverse sources, typically via APIs. Hadoop, Apache Spark).

Unstructured Data 59

Unstructured Data NoSQL Hadoop Data Lake 59

ProjectPro

DECEMBER 7, 2021

Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives. While data warehouses contain transformed data, data lakes contain unfiltered and unorganized raw data.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

FEBRUARY 8, 2023

But this data is not that easy to manage since a lot of the data that we produce today is unstructured. In fact, 95% of organizations acknowledge the need to manage unstructured raw data since it is challenging and expensive to manage and analyze, which makes it a major concern for most businesses. How Does AWS Glue Work?

AWS 98

AWS Scala Metadata Data Lake 98

Rockset

APRIL 15, 2022

It’s probably because their analytics database lacks the features necessary to deliver data-driven decisions accurately in real time. All updates are appended rather than written over existing data records. That is a tall order, but it is especially difficult when your immutable database uses clumsy hacks to ingest out-of-order data.

Analytics Application 52

Analytics Application Data Warehouse Raw Data Kafka 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. Independently create data-driven solutions that are accurate and informative.

Data Engineering 52

Data Engineering Data Engineer Engineering Amazon Web Services 52

Ascend.io

JULY 28, 2023

Data pipeline architecture is a framework that outlines the flow and management of data from its original source to its final destination within a system. This framework encompasses the steps of data ingestion, transformation, orchestration, and sharing.

Data Pipeline 52

Data Pipeline Architecture Lambda Architecture Data Architecture 52

Rockset

AUGUST 30, 2021

It becomes prohibitively complex and expensive to use a data warehouse to serve real-time analytics. Rockset: Real-time Analytics Built for the Cloud Rockset is doing for real-time analytics what Snowflake did for batch. But until this release, all these data sources involved indexing the incoming raw data on a record by record basis.

SQL 52

SQL Kafka MongoDB MySQL 52

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

RandomTrees

NOVEMBER 29, 2023

The data from many data bases are sent to the data warehouse through the ETL processes. Having said that, the governance system has all the meta data, and how on or more tables are related to each another and is defined using the governance policies a protocol how the business expects the data and how is meant to be used.

Data Warehouse 52

Data Warehouse Data Lake Database-centric Data Cleanse 52

U-Next

JUNE 29, 2022

Data ingestion can be divided into two categories: . A batch is a method of gathering and delivering huge data groups at once. Conditions can trigger data collection, scheduled or done on the fly. Due to ELT Cloud and other modern technologies, limits on data storage have become a secondary worry.

Big Data 40

Big Data Big Data Ecosystem Data Lake Raw Data 40

ProjectPro

FEBRUARY 25, 2022

Data Ingestion Data Processing Data Splitting Model Training Model Evaluation Model Deployment Monitoring Model Performance Machine Learning Pipeline Tools Machine Learning Pipeline Deployment on Different Platforms FAQs What tools exist for managing data science and machine learning pipelines?

Machine Learning 52

Machine Learning Building Amazon Web Services AWS 52