Workfall

JULY 4, 2023

With DBT, they weave powerful SQL spells to create data models that capture the essence of their organization’s information. DBT’s superpowers include seamlessly connecting with databases and data warehouses, performing amazing transformations, and effortlessly managing dependencies to ensure high-quality data.

Data Warehouse 69

Data Warehouse SQL PostgreSQL Database 69

Lyft Engineering

OCTOBER 3, 2023

High-quality data is necessary for the success of every data-driven company. It is now the norm for tech companies to have a well-developed data platform. This makes it easy for engineers to generate, transform, store, and analyze data at the petabyte scale. What and Where is Data Quality?

Big Data 84

Big Data Metadata Data Warehouse Data 84

Data Engineering Weekly

MAY 19, 2024

The article about data asset pricing is one of the comprehensive thoughts I came across about pricing models, establishing two basic factors. Data value depends on the users and the use cases Data quality is multi-dimensional, and high-quality data costs more. Register now and join us on May 22nd!

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Cloudera

OCTOBER 7, 2022

dbt allows data teams to produce trusted data sets for reporting, ML modeling, and operational workflows using SQL, with a simple workflow that follows software engineering best practices like modularity, portability, and continuous integration/continuous development (CI/CD). The Open Data Lakehouse . Introduction.

Data Warehouse 81

Data Warehouse Data Lake Government High Quality Data 81

ProjectPro

MAY 13, 2016

As the appetite for Hadoop and related big data technologies grows at an exponential rate, it is not out to spell the death of data warehousing. Data warehousing as a technology is evolving. Data Warehouse – Decide Which One to Use When Hadoop vs DataWarehouse Shocking Headlines like “Is data warehouse dead?”,

Data Warehouse 40

Data Warehouse Hadoop Unstructured Data Big Data 40

Monte Carlo

JULY 27, 2023

These specialists are also commonly referred to as data reliability engineers. To be successful in their role, data quality engineers will need to gather data quality requirements (mentioned in 65% of job postings) from relevant stakeholders. About 61% request you also have a formal computer science degree.

Engineering 52

Engineering Healthcare Scala Data Warehouse 52

Picnic Engineering

DECEMBER 12, 2023

In some instances, calculating a moving average with SQL queries or leveraging already-known demand for products ordered by our customers will be good enough. For that use case a simple SQL query can be enough. When putting bad-quality data into machine learning models, you will get bad-quality output.

Machine Learning 52

Machine Learning Deep Learning Food High Quality Data 52

Maxime Beauchemin

JANUARY 20, 2017

Data modeling is changing Typical data modeling techniques — like the star schema  — which defined our approach to data modeling for the analytics workloads typically associated with data warehouses, are less relevant than they once were.

Data Engineering 130

Data Engineering Data Engineer Engineering ETL Tools 130

Data Engineering Weekly

NOVEMBER 13, 2022

It moved from the speculation to the data engineers understanding the benefit of it and asking when we can get the implementation soon. I met many data leaders about Data Contracts, my project Schemata, and how the extended version we are building can help them create high-quality data. Not Endless DAGs!

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Towards Data Science

MARCH 29, 2023

The Data Warehouse Toolkit (Kimball & Ross) The Data Warehouse Toolkit, 3rd Edition - Kimball Group I’m not going to bury the lead. If you work in data, you at the very least need to be familiar with dimensional modeling concepts, and I personally don’t think there’s a better way than by going straight to the source.

Data Warehouse 69

Data Warehouse BI Healthcare Database 69

DataKitchen

JULY 27, 2023

Azure Databricks Delta Live Table s: These provide a more straightforward way to build and manage Data Pipelines for the latest, high-quality data in Delta Lake. SQL Server Integration Services (SSIS): You know it; your father used it. It does the job. Cost: Different tools have different pricing structures.

Data Pipeline 52

Data Pipeline BI Machine Learning Data Preparation 52

Monte Carlo

AUGUST 31, 2023

Understanding the “rise of data downtime” With a greater focus on monetizing data coupled with the ever present desire to increase data accuracy, we need to better understand some of the factors that can lead to data downtime. We’ll take a closer look at variables that can impact your data next.

Data Lake 52

Data Lake Data Pipeline Unstructured Data Data Warehouse 52

Ascend.io

FEBRUARY 28, 2024

While data engineering and Artificial Intelligence (AI) may seem like distinct fields at first glance, their symbiosis is undeniable. The foundation of any AI system is high-quality data. Here lies the critical role of data engineering: preparing and managing data to feed AI models.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Monte Carlo

AUGUST 30, 2023

Data testing is the first step in many data engineers’ journey toward reliable data. dbt (data build tool) is a SQL-based command-line tool that offers native testing features. dbt also happens to be an incredibly handy tool for running many data quality tests. This model will be saved as `.sql`

SQL 52

SQL Datasets Database High Quality Data 52

Edureka

FEBRUARY 7, 2023

They should also be proficient in programming languages such as Python , SQL , and Scala , and be familiar with big data technologies such as HDFS , Spark , and Hive. Learn programming languages: Azure Data Engineers should have a strong understanding of programming languages such as Python , SQL , and Scala.

Data Engineering 52

Data Engineering Data Engineer Engineering Programming Language 52

Monte Carlo

AUGUST 8, 2023

While this process varies from organization to organization, these unit tests are typically applied by the data engineer after they have built the data pipeline architecture. From this perspective, the data validation process looks a lot like any other DataOps process. In these cases it is important to understand data lineage.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

Monte Carlo

FEBRUARY 8, 2024

How does it impact data warehouse/lakehouse performance? If you had an Airflow job that caused your data to fail, the real culprit wouldn’t be the data but a system issue. Or if a bad dbt model or data warehouse query change ultimately broke the data product downstream, that would be considered a code issue.

BI 59

BI Data Pipeline Data Warehouse Data 59

Monte Carlo

JUNE 23, 2022

At some point in the last two decades, the size of our data became inextricably linked to our ego. We watched enviously as FAANG companies talked about optimizing hundreds of petabyes in their data lakes or data warehouses. We imagined what it would be like to manage big data quality at that scale.

Big Data 40

Big Data Management Machine Learning Data Warehouse 40

Monte Carlo

MARCH 28, 2024

For example, when you think about a data warehouse , it’s really just a codebase—primarily composed of SQL—that’s serving internal customers like other analysts, data scientists, and product managers who are using that data to go and make business decisions.

Data 52

Data High Quality Data Machine Learning Accessible 52

DataKitchen

JANUARY 3, 2022

If the IT or data engineering team can’t respond with an enabling data platform in the required time frame, the business analyst does the necessary data work themselves. This ad hoc data engineering work often means coping with numerous data tables and diverse data sets using Alteryx, SQL, Excel or similar tools. .

Business Analyst 98

Business Analyst Data Lake Consulting Data Analytics 98

DataKitchen

NOVEMBER 4, 2021

It’s too hard to change our IT data product. Can we create high-quality data in an “answer-ready” format that can address many scenarios, all with minimal keyboarding? . “I I get cut off at the knees from a data perspective, and I am getting handed a sandwich of sorts and not a good one!”.

Process 98

Process Data Process Pharmaceutical Data Lake 98

Monte Carlo

APRIL 21, 2022

Adopting a cloud data warehouse like Snowflake is an important investment for any organization that wants to get the most value out of their data. Snowflake Data Quality Feature #4: Snowsight Snowsight is another Snowflake data quality feature.

Metadata 52

Metadata Bytes Government Data 52

Monte Carlo

APRIL 24, 2023

During data ingestion, raw data is extracted from sources and ferried to either a staging server for transformation or directly into the storage level of your data stack—usually in the form of a data warehouse or data lake. There are two primary types of raw data.

Data Pipeline 52

Data Pipeline Building Data Ingestion BI 52

Monte Carlo

AUGUST 10, 2023

Time and again, we’d deliver a report, only to be notified minutes later about issues with our data. It didn’t matter how strong our ETL pipelines were or how many times we reviewed our SQL: our data just wasn’t reliable. Both terms are focused on the practice of ensuring healthy, high quality data across an organization.

Data Pipeline 98

Data Pipeline Software Engineer Software Engineering Machine Learning 98

DareData

JULY 29, 2020

With these points in mind, I argue that the biggest hurdle to the widespread adoption of these advanced techniques in the healthcare industry is not intrinsic to the industry itself, or in any way related to its practitioners or patients, but simply the current lack of high-quality data pipelines.

Healthcare 52

Healthcare Data Pipeline Medical Pipeline-centric 52

Monte Carlo

MAY 17, 2023

Data Warehouse (Or Lakehouse) Migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. “We Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly.

Data 52

Data Data Pipeline Data Engineering Data Engineer 52

Monte Carlo

MAY 17, 2023

Data warehouse (or Lakehouse) migration 34. Integrate Data Stacks Post Merger 35. Know When To Fix Vs. Refactor Data Pipelines Improve DataOps Processes 37. “We Another common breaking schema change scenario is when data teams sync their production database with their data warehouse as is the case with Freshly.

Data Pipeline 52

Data Pipeline Data Data Engineering Data Engineer 52

Monte Carlo

AUGUST 17, 2021

Their artificial intelligence data-driven platform relies on high-quality data to make coverage recommendations for customers. To achieve data trust at scale, Edward Kent, Principal Developer, and his data engineering team turned to Monte Carlo’s end-to-end data observability platform.

Insurance 40

Insurance Retail Data Pipeline Portfolio 40