Monte Carlo

MAY 28, 2024

This involves connecting to multiple data sources, using extract, transform, load ( ETL ) processes to standardize the data, and using orchestration tools to manage the flow of data so that it’s continuously and reliably imported – and readily available for analysis and decision-making.

Data Ingestion 52

Data Ingestion Architecture Designing Hadoop 52

Snowflake

AUGUST 24, 2023

The family of companies, a part of Radian Group, relies on data science and machine learning to build and deploy models using a variety of data from different sources to bring technology to all parts of the home-buyer experience. “They had to backtrack and identify the problem in our process.”

Data Pipeline 52

Data Pipeline Python Programming Language Data Validation 52

Knowledge Hut

JULY 26, 2023

Veracity meaning in big data is the degree of accuracy and trustworthiness of data, which plays a pivotal role in deriving meaningful insights and making informed decisions. This blog will delve into the importance of veracity in Big Data, exploring why accuracy matters and how it impacts decision-making processes.

Big Data 52

Big Data Data Cleanse Retail Healthcare 52

Pinterest Engineering

NOVEMBER 28, 2023

Pinterest’s real-time metrics asynchronous data processing pipeline, powering Pinterest’s time series database Goku, stood at the crossroads of opportunity. The mission was clear: identify bottlenecks, innovate relentlessly, and propel our real-time analytics processing capabilities into an era of unparalleled efficiency.

Kafka 81

Kafka Bytes Architecture Utilities 81

Databand.ai

AUGUST 30, 2023

Data quality monitoring refers to the assessment, measurement, and management of an organization’s data in terms of accuracy, consistency, and reliability. It utilizes various techniques to identify and resolve data quality issues, ensuring that high-quality data is used for business processes and decision-making.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Databand.ai

AUGUST 30, 2023

In a world where organizations rely heavily on data observability for informed decision-making, effective data testing methods are crucial to ensure high-quality standards across all stages of the data lifecycle—from data collection and storage to processing and analysis.

Data Validation 52

Data Validation Data Integration Data Database 52

Data Engineering Weekly

SEPTEMBER 24, 2023

Thoughtworks: Measuring the Value of a Data Catalog The cost & effort value proportion for a Data Catalog implementation is always questionable in a large-scale data infrastructure. Thoughtworks, in combination with Adevinta, published a three-phase approach to measure the value of a data catalog.

Data Engineering 67

Data Engineering Data Engineer Engineering Kafka 67

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. As a result, they can be slow, inefficient, and prone to errors.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. In this phase, data is collected from various sources.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

ProjectPro

FEBRUARY 8, 2023

ETL developers play a vital role in designing, implementing, and maintaining the processes that help organizations extract valuable business insights from data. ETL developer is a software developer who uses various tools and technologies to design and implement data integration processes across an organization.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 52

Knowledge Hut

MARCH 19, 2024

Under such an arrangement, the user is able to store, retrieve, and manage textual data very friendly. Strings in computer science are not just a representation of mere text but are one of the key data types enabling the process of working with text, doing encodings, storing and transmitting information between systems and platforms.

Programming Language 52

Programming Language Computer Science Java Programming 52

Snowflake

SEPTEMBER 21, 2023

When it comes to using these technologies together—specifically, managing data across them—marketing organizations unfortunately face a significant hurdle. In a traditional SaaS product, the provider stores and processes the data used within the application in their own data platform.

Google Cloud 85

Google Cloud Unstructured Data Technology Data 85

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

ProjectPro

JUNE 18, 2015

Big Data holds the promise of changing how businesses and people solve real world problems and Crowdsourcing plays a vital role in managing big data. Let’s understand how crowdsourcing big data can revolutionize business processes. When we think of big data, we think of enterprise crowdsourcing.

Big Data 52

Big Data Process Data Cleanse Data Analytics 52

Data Engineering Weekly

FEBRUARY 12, 2023

To begin, we partner with Pathway.com to launch a three-part series about unlocking stream processing , - where Pathway talks about applying linear regression & classification in real-time. Historical data processing is a rare event, where 99% of the computing happens over the last 24 hours of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Ascend.io

OCTOBER 12, 2023

A Note on Our Perspective of Data Automation When we talk about “automating data” in this article, our intent isn’t to promote the notion of replacing human involvement. Instead, we advocate for the strategic utilization of technology to streamline and enhance data-related processes.

Hospitality 52

Hospitality Data Pipeline Healthcare Data Governance 52

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Transform before Load: The ETL process conforms data to the static, predefined warehouse schema.

Data Lake 52

Data Lake ETL Tools Data Warehouse Data Pipeline 52

Knowledge Hut

SEPTEMBER 25, 2023

As an Azure Data Engineer, you will be expected to design, implement, and manage data solutions on the Microsoft Azure cloud platform. You will be in charge of creating and maintaining data pipelines, data storage solutions, data processing, and data integration to enable data-driven decision-making inside a company.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Knowledge Hut

JANUARY 25, 2024

Let's dive into the top data cleaning techniques and best practices for the future – no mess, no fuss, just pure data goodness! What is Data Cleaning? It involves removing or correcting incorrect, corrupted, improperly formatted, duplicate, or incomplete data. Why Is Data Cleaning So Important?

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Rockset

FEBRUARY 24, 2023

Introduction Let’s get this out of the way at the beginning: understanding effective streaming data architectures is hard, and understanding how to make use of streaming data for analytics is really hard. Stream processing or an OLAP database? Kafka or Kinesis ? Open source or fully managed?

Kafka 52

Kafka AWS Amazon Web Services Programming Language 52

Knowledge Hut

APRIL 19, 2023

An ETL pipeline is one of the most common solutions for the efficient processing of large data. In this article, we will discuss what is ETL, the architecture of an ETL pipeline, types of ETL pipelines, how to create an ETL pipeline, and benefits of the ETL pipeline process. What is ETL? What is an ETL Pipeline?

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

Databand.ai

AUGUST 30, 2023

Unified DataOps represents a fresh approach to managing and synchronizing data operations across several domains, including data engineering, data science, DevOps, and analytics. The goal of this strategy is to streamline the entire process of extracting insights from raw data by removing silos between teams and technologies.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Knowledge Hut

JANUARY 19, 2024

However, having a lot of data is useless if businesses can't use it to make informed, data-driven decisions by analyzing it to extract useful insights. Business intelligence (BI) is becoming more important as a result of the growing need to use data to further organizational objectives.

Business Intelligence 52

Business Intelligence BI Business Analyst Finance 52

Rockset

MARCH 1, 2023

The Challenge of Compute Contention At the heart of every real-time application you have this pattern that the data never stops coming in and requires continuous processing, and the queries never stop – whether they come from anomaly detectors that run 24x7 or end-user-facing analytics. So they are not suitable for real-time analytics.

Data Ingestion 144

Data Ingestion Database Architecture Cloud Storage 144

Pinterest Engineering

SEPTEMBER 26, 2023

While it is blessed with an abundance of data for training, it is also crucial to maintain a high data storage efficiency. Therefore, we adopted a hybrid data logging approach, with which the data is logged through both the backend service and the frontend clients. The process is captured in Figure 1.

Software Engineer 91

Software Engineer Software Engineering Machine Learning Datasets 91

Ascend.io

SEPTEMBER 19, 2023

In the world of data engineering, the ETL (Extract, Transform, Load) approach has been the cornerstone for managing and processing data. However , the traditional methods of executing ETL are increasingly struggling to meet the escalating demands of today’s data-intensive environments. The result?

ETL Tools 52

ETL Tools ETL Method Utilities Data Engineering 52

Databand.ai

AUGUST 30, 2023

The DataOps framework is a set of practices, processes, and technologies that enables organizations to improve the speed, accuracy, and reliability of their data management and analytics operations. The core philosophy of DataOps is to treat data as a valuable asset that must be managed and processed efficiently.

Data Governance 52

Data Governance Data Pipeline Government Data Cleanse 52

DataKitchen

APRIL 13, 2021

Testing and Data Observability. Process Analytics. We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Reflow — A system for incremental data processing in the cloud.

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Consulting Machine Learning Data Science Data Pipeline 120

Grouparoo

NOVEMBER 29, 2021

The data source is the location of the data that the processing will consume for data processing functions. This can be the point of origin of the data, the place of its creation. Alternatively, this can be data generated by another process and then made available for subsequent processing.

Raw Data 40

Raw Data Big Data Relational Database Data Warehouse 40

Picnic Engineering

DECEMBER 12, 2023

For individual products and warehouses, the process might seem straightforward. After revolutionizing the world of language processing and computer vision, transformer-based models are also the best in class for many forecasting challenges. 3) Even though your data is of high quality, the world can change.

Machine Learning 52

Machine Learning Deep Learning Food High Quality Data 52

Advancing Analytics: Data Engineering

JUNE 21, 2019

It’s a cornerstone of the data warehousing approach… and we shouldn’t have one. Hear me out – back in the on-premises days we had data loading processes that connect directly to our source system databases and perform huge data extract queries as the start of one long, monolithic data pipeline, resulting in our data warehouse.

Data Warehouse 52

Data Warehouse Business Intelligence Data Data Validation 52

Grouparoo

OCTOBER 6, 2021

Tianhui Michael Li The Three Rs of Data Engineering by Tobias Macey Data testing and quality Automate Your Pipeline Tests by Tom White Data Quality for Data Engineers by Katharine Jarmul Data Validation Is More Than Summary Statistics by Emily Riederer The Six Words That Will Destroy Your Career by Bartosz Mikulski Your Data Tests Failed!

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Grouparoo

DECEMBER 7, 2021

Organizations collect and leverage data on an ever-expanding basis to inform business intelligence and optimize practices. Data allows businesses to gain a greater understanding of their suppliers, customers, and internal processes. What is Data Integrity? This is distinct from factors such as data quality.

Data Integration 52

Data Integration Manufacturing ETL Tools Transportation 52

Ascend.io

JUNE 8, 2023

Whether it’s the creation of new data combinations or simply restructuring data sets for better understandability, data transformation is key to unlocking the full potential of the data. These varying definitions are based on the format, function, origin, and different uses for the data in practice.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Precisely

MARCH 14, 2023

New technologies are making it easier for customers to process increasingly large datasets more rapidly. The modules of the Data Integrity Suite seamlessly interoperate with one another to continuously build accuracy, consistency, and context in your data. Cloud-native data execution is just the beginning.

Data Integration 52

Data Integration High Quality Data BI Data 52

DataKitchen

JANUARY 23, 2023

What is data lineage? Data lineage traces data’s origin, history, and movement through various processing, storage, and analysis stages. It is used to understand the provenance of data and how it is transformed and to identify potential errors or issues. How does it compare?

Data Governance 52

Data Governance Government Data Pipeline Data 52

Cloudera

AUGUST 31, 2021

These schemas will be created based on its definitions in existing legacy data warehouses. Smart DwH Mover helps in accelerating data warehouse migration. Smart Data Validator helps in extensive data reconciliation and testing. Smart Query Convertor converts queries and views to be made compatible on CDW.

Data Warehouse 82

Data Warehouse Database-centric Metadata Cloud 82