Precisely

MARCH 14, 2023

Deploy, execute, and scale natively in modern cloud architectures To meet the need for data quality in the cloud head on, we’ve developed the Precisely Data Integrity Suite. The modules of the Data Integrity Suite seamlessly interoperate with one another to continuously build accuracy, consistency, and context in your data.

Data Integration 52

Data Integration High Quality Data BI Data 52

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. Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion 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

ProjectPro

FEBRUARY 8, 2023

ETL developer is a software developer who uses various tools and technologies to design and implement data integration processes across an organization. The role of an ETL developer is to extract data from multiple sources, transform it into a usable format and load it into a data warehouse or any other destination database.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 52

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

Databand.ai

AUGUST 30, 2023

Maintaining Data Integrity Data integrity refers to the consistency, accuracy, and reliability of data over its lifecycle. Maintaining data integrity is vital for businesses, as it ensures that data remains accurate and consistent even when it’s used, stored, or processed.

Data Validation 52

Data Validation Data Integration Data Database 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

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

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

Monte Carlo

OCTOBER 10, 2024

The role is usually on a Data Governance, Analytics Engineering, Data Engineering, or Data Science team, depending on how the data organization is structured. Attention to Detail : Critical for identifying data anomalies. Data observability tools: Monte Carlo ETL Tools : Extract, Transform, Load (e.g.,

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 52

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

Wayne Yaddow

MARCH 14, 2025

Users can apply built-in schema tests (such as not null, unique, or accepted values) or define custom SQL-based validation rules to enforce data integrity. dbt Core allows for data freshness monitoring and timeliness assessments, ensuring tables are updated within anticipated intervals in addition to standard schema validations.

Unstructured Data 52

Unstructured Data SQL Data Pipeline Data Validation 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

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 Engineer 52

Data Engineer Data Engineering Engineering Data Lake 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. The extraction process requires careful planning to ensure data integrity.

Data Cleanse 52

Data Cleanse Data Storage Raw Data Data Warehouse 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. Talend: A commercial ETL tool that supports batch and real-time data integration.It

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

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

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

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

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Ascend.io

SEPTEMBER 19, 2023

In this article, we’ll delve into what is an automated ETL pipeline, explore its advantages over traditional ETL, and discuss the inherent benefits and characteristics that make it indispensable in the data engineering toolkit. A more agile, responsive, and error-resistant data management process. The result?

ETL Tools 52

ETL Tools ETL Method Data Engineer Data Engineering 52

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. Suitable for users looking for a versatile data cleaning tool.

Data Cleanse 52

Data Cleanse Datasets Data Preparation Data Science 52

The Modern Data Company

AUGUST 13, 2024

Addressable: Data products allow for precise identification and referencing of specific data elements, improving data management, retrieval, and overall operational efficiency. Trustworthy: Maintaining high standards of data integrity and reliability is crucial.

Raw Data 52

Raw Data Metadata Datasets Utilities 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

Wayne Yaddow

FEBRUARY 26, 2025

Photo by Markus Spiske on Unsplash Introduction Senior data engineers and data scientists are increasingly incorporating artificial intelligence (AI) and machine learning (ML) into data validation procedures to increase the quality, efficiency, and scalability of data transformations and conversions.

Data Engineer 52

Data Engineer Data Engineering Engineering Data Pipeline 52

Picnic Engineering

DECEMBER 12, 2023

Outlier Detection “pandera is an open source project that provides a flexible and expressive API for performing data validation on dataframe-like objects to make data processing pipelines more readable and robust.” —  [link] We use Pandera to ensure data integrity and flag outliers in our machine-learning pipeline.

Machine Learning 52

Machine Learning Deep Learning Food Software Engineer 52

ProjectPro

FEBRUARY 16, 2023

Knowing SQL helps data engineers optimize data infrastructures for better performance and efficiency and also develop more effective data models and data warehousing solutions. Data integration will become highly significant as the amount of data globally grows in volume, variety, and complexity.

Data Engineer 52

Data Engineer Data Engineering SQL Engineering 52

ProjectPro

JANUARY 31, 2023

Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes. Data Processing: This is the final step in deploying a big data model. How to avoid the same.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

AUGUST 21, 2015

Big Data Hadoop Interview Questions and Answers These are Hadoop Basic Interview Questions and Answers for freshers and experienced. Hadoop vs RDBMS Criteria Hadoop RDBMS Datatypes Processes semi-structured and unstructured data. Processes structured data. More data needs to be substantiated.

Hadoop 40

Hadoop Big Data Metadata Java 40

Striim

AUGUST 28, 2024

These roles will span various sectors, including data science, AI ethics, machine learning engineering, and AI-related research and development. Real-Time Data — The Missing Link What is Real-Time Data? Misconception: Batch Processing Suffices Objection: Many AI/ML tasks can be handled with batch processing.

Healthcare 96

Healthcare Retail Algorithm Finance 96

Striim

JUNE 17, 2024

These roles will span various sectors, including data science, AI ethics, machine learning engineering, and AI-related research and development. Real-Time Data — The Missing Link What is Real-Time Data? Tools like Apache Beam and Spark Streaming provide mechanisms for real-time data validation and cleansing.

Algorithm 52

Algorithm Retail Data Ingestion Healthcare 52

Ascend.io

OCTOBER 12, 2023

This could range from speeding up data entry processes, to ensuring data consistency, to near real-time data analysis. Whether it’s a 20% reduction in data processing time or a 15% increase in data accuracy, having measurable outcomes can guide your journey. How will your data sources grow?

Hospitality 52

Hospitality Data Pipeline Healthcare Data Governance 52

RandomTrees

MARCH 17, 2025

Businesses are no longer just collecting data; they are looking to connect it , transform it , and leverage it for valuable insights in real-time. This is where Airbyte , the open-source data integration platform, is redefining the game. Airbyte supports both batch and real-time data integration.

Data Integration 52

Data Integration ETL Tools Data Workflow Data Pipeline 52

DataKitchen

JANUARY 23, 2023

Verification is checking that data is accurate, complete, and consistent with its specifications or documentation. This includes checking for errors, inconsistencies, or missing values and can be done through various methods such as data profiling, data validation, and data quality assessments.

Data Governance 52

Data Governance Government Data Pipeline Data 52