Databand.ai

AUGUST 30, 2023

Here are some of the reasons why DataOps tools are important: Improved Collaboration DataOps tools enable better collaboration between data teams, including data engineers, data scientists, and data analysts.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Data Engineering Podcast

OCTOBER 15, 2021

In this episode they discuss the recent work that has been done by the community, how their work is building on top of that foundation, and how you can get started with DataHub for your own work to manage data discovery today. Are you bored with writing scripts to move data into SaaS tools like Salesforce, Marketo, or Facebook Ads?

Metadata 100

Metadata BI Data Warehouse Government 100

Data Engineering Podcast

APRIL 5, 2021

She explains how the design of the platform is informed by the needs of managing data projects for large and small teams across her previous roles, how it integrates with your existing systems, and how it can work to bring everyone onto the same page. RudderStack’s smart customer data pipeline is warehouse-first.

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

AltexSoft

MARCH 14, 2023

As the volume and complexity of data continue to grow, organizations seek faster, more efficient, and cost-effective ways to manage and analyze data. In recent years, cloud-based data warehouses have revolutionized data processing with their advanced massively parallel processing (MPP) capabilities and SQL support.

IT 59

IT Data Warehouse Data Governance Data Lake 59

Monte Carlo

JULY 27, 2023

Data quality engineers also need to have experience operating in cloud environments and using many of the modern data stack tools that are utilized in building and maintaining data pipelines. 78% of job postings referenced at least part of their environment was in a modern data warehouse, lake, or lakehouse.

Engineering 52

Engineering Healthcare Scala Data Warehouse 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 supports the data engineering lifecycle by bringing automation to data pipelines.

Data Pipeline 52

Data Pipeline Data Workflow Data Data Governance 52

Ascend.io

DECEMBER 19, 2023

You’re extracting and loading data first, then transforming it in Snowflake’s cloud data warehouse. These features make Snowflake a great solution for data engineering, but the question arises: why might you still need a separate ETL tool? that you can combine to create custom data workflows.

ETL Tools 52

ETL Tools IT Data Pipeline Data Warehouse 52

Monte Carlo

MARCH 24, 2023

On the face of it, data migration should be fairly straightforward – just pick up your data and move it where it needs to go. After all, as data engineers aren’t we migrating data all the time within our ETL pipelines? Data corruption Like a backup hard drive or SD card that refuses to work…on a much bigger scale.

Data Warehouse 52

Data Warehouse AWS Cloud Data Governance 52

Monte Carlo

SEPTEMBER 20, 2022

Here’s how Prefect , Series B startup and creator of the popular data orchestration tool, harnessed the power of data observability to preserve headcount, improve data quality and reduce time to detection and resolution for data incidents. But a growing company means growing data needs. Scaling data governance.

Big Data 52

Big Data Data Warehouse Data Data Governance 52

Monte Carlo

JUNE 14, 2023

For example, data may be extracted from multiple sources, reorganized, and joined at different times before ultimately being served to its final destination. Why is data pipeline architecture important? Data then, and even today for some organizations, was primarily hosted in on-premises databases with non-scalable storage.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

Databand.ai

DECEMBER 29, 2022

Follow Ravit on LinkedIn 5) Priya Krishnan Head of Product Management, Data and AI at IBM Priya is an innovative, customer-focused, data-driven product executive with over 16 years of experience in global product management, strategy, and GTM roles to commercialize and monetize in-demand enterprise solutions.

BI 52

BI Consulting Data Science Data Governance 52

phData: Data Engineering

JANUARY 3, 2022

Software engineering practices define how to reliably and effectively build software and data products, delivering value faster to your customers. In this post, we will explore the complexities involved with software engineering with a focus on data engineering and data operations (DataOps).

IT 52

IT AWS Software Engineer Software Engineering 52

DataKitchen

APRIL 13, 2021

We have also included vendors for the specific use cases of ModelOps, MLOps, DataGovOps and DataSecOps which apply DataOps principles to machine learning, AI, data governance, and data security operations. . Piperr.io — Pre-built data pipelines across enterprise stakeholders, from IT to analytics, tech, data science and LoBs.

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Consulting Machine Learning Data Science Data Pipeline 120