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 92

Big Data Metadata Data Warehouse Data 92

Precisely

JULY 18, 2024

While data fabric is not a standalone solution, critical capabilities that you can address today to prepare for a data fabric include automated data integration, metadata management, centralized data governance, and self-service access by consumers. Increase metadata maturity.

Data Management 52

Data Management Management Metadata Database-centric 52

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. Strong analytical and technical skills to address sophisticated issues.

Engineering 52

Engineering Healthcare Data Warehouse Scala 52

Edureka

FEBRUARY 27, 2023

Here are some of the common types: Data Warehouses: A data warehouse is a centralized repository of information that can be used for reporting and analysis. Data warehouses typically contain historical data that can be used to track trends over time.

Business Intelligence 52

Business Intelligence BI Data Mining Data Warehouse 52

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.

Data Engineering 52

Data Engineering Data Engineer Engineering Kafka 52

Monte Carlo

MAY 25, 2023

Automated data orchestration removes data bottlenecks by eliminating the need for manual data preparation, enabling analysts to both extract and activate data in real-time. Improved data governance. Data orchestration enables data leaders to remove data silos without depending on manual migration.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 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

Databand.ai

JULY 5, 2023

Data catalog and lineage tools: These tools provide visibility into data lineage by tracking the origin, transformation, and consumption of data across the data pipeline. They help organizations understand the dependencies between data sources, processes, and systems, enabling better data governance and impact analysis.

Data Pipeline 40

Data Pipeline Data Lake Data Warehouse Datasets 40

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

Databand.ai

JANUARY 24, 2023

DataOps helps ensure organizations make decisions based on sound data. Previously, organizations have grabbed their full dataset across multiple environments, put it all into a data warehouse, and surfaced information from there. Who’s Involved in a DataOps Team?

Retail 52

Retail Banking Government Manufacturing 52

AltexSoft

AUGUST 22, 2022

A data fabric is an architecture design presented as an integration and orchestration layer built on top of multiple disjointed data sources like relational databases , data warehouses , data lakes, data marts , IoT , legacy systems, etc., to provide a unified view of all enterprise data.

Architecture 52

Architecture Metadata Data Lake Machine Learning 52

Monte Carlo

AUGUST 30, 2023

Often, teams run custom data tests as part of a deployment pipeline, or scheduled on production systems via job schedulers like Apache Airflow, dbt Cloud, or via in-built schedulers in your data warehouse solution. Also, remember data governance. Here are some common use cases for dbt tests.

SQL 52

SQL Datasets Database High Quality Data 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 Engineering Data Engineer Data 52