Advancing Analytics: Data Engineering

JUNE 21, 2019

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. Till next time.

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Data Warehouse Business Intelligence Data Data Validation 52

Data Engineering Podcast

JUNE 16, 2019

This conversation was useful for getting a better idea of the challenges that exist in large scale data analytics, and the current state of the tradeoffs between data lakes and data warehouses in the cloud. What are some of the common antipatterns in data lake implementations and how does Delta Lake address them?

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Data Lake Lambda Architecture Data Warehouse Hadoop 100

Cloudera

AUGUST 31, 2021

Cloudera and Accenture demonstrate strength in their relationship with an accelerator called the Smart Data Transition Toolkit for migration of legacy data warehouses into Cloudera Data Platform. Accenture’s Smart Data Transition Toolkit . Are you looking for your data warehouse to support the hybrid multi-cloud?

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Data Warehouse Database-centric Metadata Cloud 82

Ascend.io

NOVEMBER 14, 2024

Snowflake and Azure Synapse offer powerful data warehousing solutions that simplify data integration and analysis by providing elastic scaling and optimized query performance.

Data Pipeline 52

Data Pipeline Amazon Web Services Data Integration Data 52

Data Engineering Weekly

MAY 16, 2023

It involves thorough checks and balances, including data validation, error detection, and possibly manual review. Data Testing vs. These design patterns lead to disjointed data quality tools that add more cost to the pipeline operation than solving the problem. Why I’m making this claim? How to Fix It? Stay Tuned.

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Engineering Kafka Data Pipeline Data Warehouse 111

Rockset

MARCH 1, 2023

If such query workloads create additional data lags then it will actively cause more harm by increasing your blind spot at the exact wrong time, the time when fraud is being perpetrated. OLTP databases aren’t built to ingest massive volumes of data streams and perform stream processing on incoming datasets.

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Data Ingestion Database Architecture SQL 144

Airbnb Tech

NOVEMBER 28, 2023

However, for all of our uncertified data, which remained the majority of our offline data, we lacked visibility into its quality and didn’t have clear mechanisms for up-leveling it. How could we scale the hard-fought wins and best practices of Midas across our entire data warehouse?

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Data Warehouse Metadata Data Certification 98

U-Next

MARCH 7, 2023

In software engineering, data modeling involves applying formal techniques to create a data model for an information system. Dimensional modeling refers to the use of fact and dimension tables to keep a record of historical data in data warehouses. How does a Data Analysis project work?

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Databand.ai

AUGUST 30, 2023

Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. DataOps tools help ensure data quality by providing features like data profiling, data validation, and data cleansing.

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Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Advancing Analytics: Data Engineering

JUNE 10, 2019

After the horror that was the “data silo” days, with clumps of data living in Access databases, Excel spreadsheets and isolated data stores, we’ve had a pretty good run with the classic Kimball data warehouse. However, a data warehouse is a large, sanitary data store.

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Data Warehouse Data Lake Data Data Validation 52

Ascend.io

AUGUST 31, 2023

Secondly , the rise of data lakes that catalyzed the transition from ELT to ELT and paved the way for niche paradigms such as Reverse ETL and Zero-ETL. Still, these methods have been overshadowed by EtLT — the predominant approach reshaping today’s data landscape. Read More: What is ETL?

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Data Lake Data Warehouse ETL Tools Data Pipeline 52

ProjectPro

FEBRUARY 8, 2023

ETL stands for Extract, Transform, and Load, which involves extracting data from various sources, transforming the data into a format suitable for analysis, and loading the data into a destination system such as a data warehouse. ETL developers play a significant role in performing all these tasks.

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DataKitchen

APRIL 13, 2021

RightData – A self-service suite of applications that help you achieve Data Quality Assurance, Data Integrity Audit and Continuous Data Quality Control with automated validation and reconciliation capabilities. QuerySurge – Continuously detect data issues in your delivery pipelines. Production Monitoring Only.

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RandomTrees

SEPTEMBER 6, 2020

Snowflake Overview A data warehouse is a critical part of any business organization. Lot of cloud-based data warehouses are available in the market today, out of which let us focus on Snowflake. Snowflake is an analytical data warehouse that is provided as Software-as-a-Service (SaaS).

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Cloud Storage Data Ingestion Data Cleanse Data Warehouse 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 data is loaded as-is, without any transformation.

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Data Cleanse Data Storage Raw Data Data Warehouse 52

Edureka

AUGUST 2, 2023

Data integration and transformation: Before analysis, data must frequently be translated into a standard format. Data processing analysts harmonise many data sources for integration into a single data repository by converting the data into a standardised structure.

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Data Process Process Data Cleanse Data Mining 52

Databand.ai

JUNE 20, 2023

Niv Sluzki June 20, 2023 What Is Data Integrity? Data integrity refers to the overall accuracy, consistency, and reliability of data stored in a database, data warehouse, or any other information storage system. 4 Ways to Prevent and Resolve Data Integrity Issues 1.

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Data Integration Data Validation Pharmaceutical Data Cleanse 52

Databand.ai

JULY 6, 2023

Data Integrity Testing: Goals, Process, and Best Practices Niv Sluzki July 6, 2023 What Is Data Integrity Testing? Data integrity testing refers to the process of validating the accuracy, consistency, and reliability of data stored in databases, data warehouses, or other data storage systems.

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Data Integration Process Data Validation Data Governance 52

AltexSoft

NOVEMBER 23, 2021

Before we get into more detail, let’s determine how data virtualization is different from another, more common data integration technique — data consolidation. Data virtualization vs data consolidation. The example of a typical two-tier architecture with a data lake and data warehouses and several ETL processes.

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Process Data Lake Metadata Data Warehouse 69

Knowledge Hut

APRIL 19, 2023

It is the process of extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse. ETL is used to integrate data from different sources and formats into a single target for analysis. What is an ETL Pipeline?

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Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

Airbnb Tech

NOVEMBER 24, 2020

During this transformation, Airbnb experienced the typical growth challenges that most companies do, including those that affect the data warehouse. In the first post of this series, we shared an overview of how we evolved our organization and technology standards to address the data quality challenges faced during hyper growth.

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Data Warehouse Certification Data Pipeline Data 52

Striim

JANUARY 4, 2023

Companies need to analyze large volumes of datasets, leading to an increase in data producers and consumers within their IT infrastructures. These companies collect data from production applications and B2B SaaS tools (e.g., This data makes its way into a data repository, like a data warehouse (e.g.,

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dbt Developer Hub

MARCH 23, 2023

Optional: Disable the print_table() command, so the model can be materialized on your data warehouse. With this detailed report, it becomes easier for the AE to find out what could be going wrong with the data refactoring workflow, so the issue can be directly investigated and solved.

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Data Warehouse Generalist SQL Coding 64

Meltano

JANUARY 25, 2023

Completely Versioned Data Stacks Modern Data Stacks in a Box with DuckDB by Jacob Matson “Why build a bundled Modern Data Stack on a single machine, rather than on multiple machines and on a data warehouse? There are many advantages!

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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. Workaround: Use Git branches, tagging, and commit messages to trackchanges.

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Unstructured Data SQL Data Pipeline Data Validation 52

Monte Carlo

APRIL 7, 2025

Its strong on structure, with built-in type checks and data validation, so you dont end up with mystery bugs halfway through your pipeline. Tech stack compatibility: Look for tools that integrate well with your existing data warehouse (like Snowflake or BigQuery), version control, and other parts of your ML pipeline.

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Python Machine Learning Data Warehouse Data Validation 52

Wayne Yaddow

FEBRUARY 18, 2025

By understanding the differences between transformation and conversion testing and the unique strengths of each tool, organizations can design more reliable, efficient, and scalable data validation frameworks to support their data pipelines.

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Data Pipeline SQL Raw Data Python 52

Monte Carlo

OCTOBER 1, 2020

In this article we introduce “Key Assets”, a new approach taken by the best data teams to surface your most important data assets for quick and reliable insights. Have you been 3/4ths of the way done with a data warehouse migration only to discover that you don’t know which data assets are right and which ones are wrong?

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Data Warehouse Datasets Machine Learning Data Validation 40

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. 69 The End of ETL as We Know It Use events from the product to notify data systems of changes.

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Grouparoo

DECEMBER 7, 2021

Transmitting data across multiple paths can identify the compromise of one path or a path exhibiting erroneous behavior and corrupting data. Data validation rules can identify gross errors and inconsistencies within the data set. Read more about our Reverse ETL Tools. featured image via unsplash

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Data Integration Manufacturing ETL Tools Transportation 52

Monte Carlo

MAY 26, 2023

This could be the result of a sync issue, the type of information each collects, or how each platform treats different data types. Having the data warehouse as a central source of truth can help present a more consistent view of the campaign. Looker also has a semantic layer called LookML.

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Data Warehouse Datasets Data Validation Data 52

Databand.ai

JULY 19, 2023

Despite these challenges, proper data acquisition is essential to ensure the data’s integrity and usefulness. Data Validation In this phase, the data that has been acquired is checked for accuracy and consistency.

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Data Ingestion Process Data Cleanse Data Governance 52

Data Engineering Weekly

OCTOBER 30, 2022

DuckDB is gaining much attention on this promise, and the Dagster team writes about its experimental data warehouse built on top of DuckDB, Parquet, and Dagster. link] Sponsored: Why You Should Care About Dimensional Data Modeling It's easy to overlook all of the magic that happens inside the data warehouse.

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dbt Developer Hub

APRIL 24, 2023

To that end, we needed to have both the legacy Alteryx workflow output table and the refactored dbt model materialized in the project’s data warehouse. Then we used the macros available in audit_helper to compare query results, data types, column values, row numbers and many more things that are available within the package.

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BI Data Workflow SQL Data Pipeline 83

Monte Carlo

MARCH 24, 2023

Database differences and schema management Each database, even in the cloud, stores values a little differently–but those little changes can be big data migration risks. For example, one data leader gave us the example of how two data warehouse store dollar amounts differently.

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Data Warehouse AWS Cloud Database 52

Monte Carlo

FEBRUARY 15, 2023

But, while this can provide some much-needed context, it doesn’t provide the granularity data teams need to remediate the data problems they uncover—or prevent them from happening again in the future. In the context of data pipelines, column level lineage traces the relationships across and between upstream source systems (i.e.,

Data Validation 52

Data Validation Data Data Engineer Data Engineering 52

Monte Carlo

FEBRUARY 15, 2023

But, while this can provide some much-needed context, it doesn’t provide the granularity data teams need to remediate the data problems they uncover—or prevent them from happening again in the future. In the context of data pipelines, column level lineage traces the relationships across and between upstream source systems (i.e.,

Data Validation 52

Data Validation Data Engineer Data Engineering Data 52