Snowflake

SEPTEMBER 21, 2023

Providers benefit from monetization and distribution across the Data Cloud, secured IP, improved margins, and accelerated procurement cycles. Leaders and ones to watch in the Modern Marketing Data Stack are already offering Snowflake Native Apps on Snowflake Marketplace. This is only the start of Snowflake Native Apps.

Google Cloud 114

Google Cloud Unstructured Data Technology Data 114

Databand.ai

AUGUST 30, 2023

DataOps , short for data operations, is an emerging discipline that focuses on improving the collaboration, integration, and automation of data processes across an organization. Accelerated Data Analytics DataOps tools help automate and streamline various data processes, leading to faster and more efficient data analytics.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 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 68

Data Engineering Data Engineer Engineering Kafka 68

Wayne Yaddow

MARCH 14, 2025

Executing dbt docs creates an interactive, automatically generated data model catalog that delineates linkages, transformations, and test coverageessential for collaboration among data engineers, analysts, and business teams.

Unstructured Data 52

Unstructured Data SQL Data Pipeline Data Validation 52

Precisely

APRIL 30, 2024

RPA is best suited for simple tasks involving consistent data. It’s challenged by complex data processes and dynamic environments Complete automation platforms are the best solutions for complex data processes. There are limitations you need to understand before undertaking RPA initiatives.

Unstructured Data 59

Unstructured Data Government Data Validation Programming 59

Knowledge Hut

JULY 26, 2023

What is Big Data? Big Data is the term used to describe extraordinarily massive and complicated datasets that are difficult to manage, handle, or analyze using conventional data processing methods. The real-time or near-real-time nature of Big Data poses challenges in capturing and processing data rapidly.

Big Data 52

Big Data Data Cleanse Retail Healthcare 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. The team first tried to use a different vendor to store and process billions of rows.

Data Pipeline 52

Data Pipeline Python Programming Language Data Validation 52

DataKitchen

APRIL 13, 2021

Composable Analytics — A DataOps Enterprise Platform with built-in services for data orchestration, automation, and analytics. Reflow — A system for incremental data processing in the cloud. Dagster / ElementL — A data orchestrator for machine learning, analytics, and ETL. .

Consulting 120

Consulting Machine Learning Data Science Data Pipeline 120

Rockset

MARCH 1, 2023

RocksDB Cloud allowed Rockset to completely separate the “performance layer” of the data management system responsible for fast and efficient data processing from the “durability layer” responsible for ensuring data is never lost.

Data Ingestion 144

Data Ingestion Database Architecture SQL 144

Pinterest Engineering

SEPTEMBER 26, 2023

As part of the data processing, we utilize batch inference to get model scores from the previous version of the production model and enrich the training dataset. Performance Validation We validate the auto-retraining quality at two places throughout the pipeline.

Software Engineer 105

Software Engineer Software Engineering Machine Learning Datasets 105

Precisely

MAY 2, 2024

These processes are prone to errors, and poor-quality data can lead to delays in order processing and a host of downstream shipping and invoicing problems that put your customer relationships at risk. It’s clear that automation transforms the way we work, in SAP customer master data processes and beyond.

Coding Skills 69

Coding Skills Food Data Portfolio 69

Databand.ai

AUGUST 30, 2023

Data Quality Rules Data quality rules are predefined criteria that your data must meet to ensure its accuracy, completeness, consistency, and reliability. These rules are essential for maintaining high-quality data and can be enforced using data validation, transformation, or cleansing processes.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

ProjectPro

FEBRUARY 8, 2023

Data Integration and Transformation, A good understanding of various data integration and transformation techniques, like normalization, data cleansing, data validation, and data mapping, is necessary to become an ETL developer. Extract, transform, and load data into a target system.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 52

Databand.ai

AUGUST 30, 2023

By identifying bottlenecks, inefficiencies, and performance issues, data testing methods enable businesses to optimize their data systems and applications to deliver optimal performance. This results in faster, more efficient data processing, cost savings, and improved user experience.

Data Validation 52

Data Validation Data Integration Data Database 52

Databand.ai

JULY 19, 2023

Whether it is intended for analytics purposes, application development, or machine learning, the aim of data ingestion is to ensure that data is accurate, consistent, and ready to be utilized. It is a crucial step in the data processing pipeline, and without it, we’d be lost in a sea of unusable data.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 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. This can be achieved through data cleansing and data validation.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 52

Advancing Analytics: Data Engineering

JUNE 21, 2019

To counter this, we build a very lightweight data transfer to land the data into unstructured storage before performing any data validation, checks etc. Then, if there are any problems with our downstream data processing, we can go back to this landed data, not back to our source.

Data Warehouse 52

Data Warehouse Business Intelligence Data Data Validation 52

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 Data Engineering 67

Monte Carlo

OCTOBER 10, 2024

Attention to Detail : Critical for identifying data anomalies. Tools : Familiarity with data validation tools, data wrangling tools like Pandas , and platforms such as AWS , Google Cloud , or Azure. Data observability tools: Monte Carlo ETL Tools : Extract, Transform, Load (e.g., Informatica , Talend ).

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

Databand.ai

AUGUST 30, 2023

Challenges of Legacy Data Architectures Some of the main challenges associated with legacy data architectures include: Lack of flexibility: Traditional data architectures are often rigid and inflexible, making it difficult to adapt to changing business needs and incorporate new data sources or technologies.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 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

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

Precisely

JANUARY 31, 2024

In this survey of more than 150 SAP® IT stakeholders and business users, we found that over 85% of companies recognize the value of automating their SAP business processes and their associated SAP data processes. This process is notoriously complex and error-prone.

Process 59

Process Data Validation Algorithm Technology 59

Rockset

FEBRUARY 24, 2023

Strong schema support : Avro has a well-defined schema that allows for type safety and strong data validation. Sample use case: Avro is a good choice for big data platforms that need to process and analyze large volumes of log data.

Kafka 52

Kafka AWS Amazon Web Services Programming Language 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

Ascend.io

AUGUST 31, 2023

Phase 2: Consolidate ETL and ELT The costs of cloud data warehouses have dropped sufficiently to where the maintenance of a separate data lake makes less economic sense. In addition , some cloud data warehouses like Snowflake are expanding their features to match the diverse and flexible data processing methodologies of data lakes.

Data Lake 52

Data Lake Data Warehouse ETL Tools 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

MARCH 19, 2024

String handling forms the backbone of supporting many tasks that relate to complex text processing, data validation, formatting, and parsing. Application of String Strings are the basic data types in computer programming and they are used in very many domains. Concatenate : Combines two or more strings into one.

Programming Language 52

Programming Language Computer Science Java Programming 52

Knowledge Hut

APRIL 19, 2023

Data validation: Data validation as it goes through the pipeline to ensure it meets the necessary quality standards and is appropriate for the final goal. This may include checking for missing data, incorrect values, and other issues. This will make it easier to identify and resolve any issues that arise.

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

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

Knowledge Hut

JANUARY 25, 2024

Fixing Errors: The Gremlin Hunt Errors in data are like hidden gremlins. Use spell-checkers and data validation checks to uncover and fix them. Automated data validation tools can also help detect anomalies, outliers, and inconsistencies. Generates clean scripts for further data processing.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

Databand.ai

AUGUST 30, 2023

These experts will need to combine their expertise in data processing, storage, transformation, modeling, visualization, and machine learning algorithms, working together on a unified platform or toolset.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Grouparoo

DECEMBER 7, 2021

However, this leveraging of information will not be effective unless the organization can preserve the integrity of the underlying data over its lifetime. Integrity is a critical aspect of data processing; if the integrity of the data is unknown, the trustworthiness of the information it contains is unknown.

Data Integration 52

Data Integration Manufacturing ETL Tools Transportation 52

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

Ascend.io

SEPTEMBER 19, 2023

By taking over mundane and repetitive chores (sometimes referred to as “ custodial engineering ”), they free up data engineers to channel their expertise towards more complex, strategic challenges — challenges that require critical thinking, creativity, and domain knowledge.

ETL Tools 52

ETL Tools ETL Method Data Engineering Data Engineer 52

Databand.ai

AUGUST 30, 2023

One key aspect of data governance is data quality management. This involves the implementation of processes and controls that help ensure the accuracy, completeness, and consistency of data. Data quality management can include data validation, data cleansing, and the enforcement of data standards.

Data Governance 52

Data Governance Data Pipeline Government Business Analyst 52

ProjectPro

JUNE 18, 2015

The goal of a big data crowdsourcing model is to accomplish the given tasks quickly and effectively at a lower cost. Crowdsource workers can perform several tasks for big data operations like- data cleansing, data validation, data tagging, normalization and data entry.

Big Data 52

Big Data Process Data Cleanse Data Analytics 52