Databand.ai

AUGUST 30, 2023

DataOps is a collaborative approach to data management that combines the agility of DevOps with the power of data analytics. It aims to streamline data ingestion, processing, and analytics by automating and integrating various data workflows.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

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. What portions of the data workflow is Atlan responsible for?

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Data Engineering Podcast

MAY 3, 2021

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. How does security factor into the design of robust DataOps systems?

Data Warehouse 100

Data Warehouse Data Pipeline BI Metadata 100

Workfall

JUNE 12, 2023

It facilitates data synchronisation, replication, real-time analytics, and event-driven processing, empowering data-driven decision-making and operational efficiency. Types of CDC Audit Columns: This method involves using designated columns within tables to track incremental changes.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Data Engineering Podcast

AUGUST 5, 2019

Finding the data that you need is tricky, and Amundsen will help you solve that problem. And as your data grows in volume and complexity, there are foundational principles that you can follow to keep data workflows streamlined. Can you start by explaining what Amundsen is and the problems that it was designed to address?

PostgreSQL 100

PostgreSQL MongoDB Metadata Media 100

Hepta Analytics

FEBRUARY 14, 2022

We already had a script that downloaded a csv file, processed the data and pushed the data to postgres database. This week, we got to think about our data ingestion design. The data is in use and is difficult to update. Designing the workflow is dependent on the use case.

Data Ingestion 52

Data Ingestion Data Engineering Data Engineer Engineering 52

Knowledge Hut

SEPTEMBER 29, 2023

Why Should You Get an Azure Data Engineer Certification? Becoming an Azure data engineer allows you to seamlessly blend the roles of a data analyst and a data scientist. One of the pivotal responsibilities is managing data workflows and pipelines, a core aspect of a data engineer's role.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Cloudera

FEBRUARY 11, 2019

Cloudera provides a unified platform with multiple data apps and tools, big data management, hybrid cloud deployment flexibility, admin tools for platform provisioning and control, and a shared data experience for centralized security, governance, and metadata management. 3. Expansion beyond core data management.

Data Management 42

Data Management Management Metadata Government 42

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. This left Dylan’s team with a gap to fill.

Big Data 52

Big Data Data Warehouse Data Data Governance 52

Cloudera

AUGUST 21, 2020

The governance aspect is perhaps even more important and businesses need to be able to understand where the data comes from. Data lineage, personally identifiable information or PPI and metadata all fall under a broad data governance banner which is critically important in terms of what needs to be protected and mapped out.

Banking 59

Banking Kafka Cloud Storage Government 59

Data Engineering Podcast

DECEMBER 18, 2022

In this episode Abe Gong brings his experiences with the Great Expectations project and community to discuss the technical and organizational considerations involved in implementing these constraints to your data workflows. Atlan is the metadata hub for your data ecosystem. Missing data? Stale dashboards?

Metadata 130

Metadata Business Intelligence Data Lake BI 130

Data Engineering Podcast

NOVEMBER 26, 2023

Data lakes are notoriously complex. For data engineers who battle to build and scale high quality data workflows on the data lake, Starburst powers petabyte-scale SQL analytics fast, at a fraction of the cost of traditional methods, so that you can meet all your data needs ranging from AI to data applications to complete analytics.

Architecture 147

Architecture Data Lake High Quality Data SQL 147

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. By improving data quality, organizations can generate more accurate and reliable insights from their data analytics processes.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Ascend.io

DECEMBER 11, 2023

Data orchestration is the process of efficiently coordinating the movement and processing of data across multiple, disparate systems and services within a company. So, why is data orchestration a big deal? It automates and optimizes data processes, reducing manual effort and the likelihood of errors.

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

Data Engineering Weekly

JANUARY 15, 2023

SiliconANGLE theCUBE: Analyst Predictions 2023 - The Future of Data Management By far one of the best analyses of trends in Data Management. 2023 predictions from the panel are; Unified metadata becomes kingmaker. The blog narrates the causal machine-learning approach in designing promotional artwork for their TV shows.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Netflix Tech

NOVEMBER 20, 2023

If the pipeline has multiple stages or many downstream workflows, users have to manually create backfill workflows for each of them and that requires significant manual work. The incremental processing solution (IPS) described here has been designed to address the above problems.

Process 84

Process Data Pipeline Datasets SQL 84

AltexSoft

NOVEMBER 7, 2022

Before we start, all those who are new to data engineering can watch our video explaining its general concepts. How data engineering works. Apache Airflow is an open-source Python -based workflow orchestrator that enables you to design, schedule, and monitor data pipelines. Metadata database.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 52

Databand.ai

AUGUST 30, 2023

Integrating these principles with data operation-specific requirements creates a more agile atmosphere that supports faster development cycles while maintaining high quality standards. Technical Challenges Choosing appropriate tools and technologies is critical for streamlining data workflows across the organization.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Monte Carlo

JUNE 14, 2023

In this post, we will help you quickly level up your overall knowledge of data pipeline architecture by reviewing: Table of Contents What is data pipeline architecture? Why is data pipeline architecture important? What is data pipeline architecture? It is like a smart scheduler for your data workflows.

Data Pipeline 52

Data Pipeline Architecture Data Lake Data Warehouse 52

AltexSoft

JUNE 7, 2021

A HDFS Master Node, called a NameNode , keeps metadata with critical information about system files (like their names, locations, number of data blocks in the file, etc.) and keeps track of storage capacity, a volume of data being transferred, etc. Data storage options. Among solutions facilitation data management are.

Big Data Tools 98

Big Data Tools Hadoop Big Data Database-centric 98

Netflix Tech

OCTOBER 27, 2020

For example, in order to enhance our user experience, one online application fetches subscribers’ preferences data to recommend movies and TV shows. The data warehouse is not designed to serve point requests from microservices with low latency. Figure 1 shows how we use Bulldozer to move data at Netflix.

Data Warehouse 97

Data Warehouse Datasets Data Big Data 97

Netflix Tech

OCTOBER 18, 2022

With the high growth of workflows in the past few years?—?increasing increasing at > 100% a year, the need for a scalable data workflow orchestrator has become paramount for Netflix’s business needs. A workflow instance is an execution of a workflow, similarly, an execution of a step is called a step instance.

Java 81

Java Data Machine Learning Systems 81

AltexSoft

SEPTEMBER 21, 2023

These indices are specially designed data structures that map out the data for rapid searches, allowing for the retrieval of queries in milliseconds. As a result, Elasticsearch is exceptionally efficient in managing structured and unstructured data. Fluentd is a data collector and a lighter-weight alternative to Logstash.

Engineering 52

Engineering NoSQL Programming Language Java 52

Ascend.io

MAY 19, 2023

The holistic approach of the post-modern data stack translates into numerous benefits: First, it accelerates pinpointing and troubleshooting pipeline hotspots with a single console that observes the entire data pipeline and all its processes. Second, it enhances governance and security. Lastly, it improves efficiency and scalability.

Data Pipeline 52

Data Pipeline Data Engineering Data Engineer Media 52

Data Engineering Podcast

SEPTEMBER 18, 2022

Summary There is a constant tension in business data between growing siloes, and breaking them down. Even when a tool is designed to integrate information as a guard against data isolation, it can easily become a silo of its own, where you have to make a point of using it to seek out information.

Building 100

Building Metadata MongoDB MySQL 100

Ascend.io

MARCH 17, 2023

Results React drastically reduced design time and load time, while boosting application performance. Automation Yields High-Leverage Capabilities In the examples above, automation based on a new type of controller designed for each domain is the key to unlocking unprecedented efficiency.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Ascend.io

MARCH 17, 2023

Results React drastically reduced design time and load time, while boosting application performance. Automation Yields High-Leverage Capabilities In the examples above, automation based on a new type of controller designed for each domain is the key to unlocking unprecedented efficiency.

Data Pipeline 52

Data Pipeline Datasets Data Software Engineer 52

Databand.ai

DECEMBER 29, 2022

Sudhir has built and led several high performing, global data teams and designed several interfaces and services across Microsoft and Google cloud products. She’s also the co-author of “Mothers of Data Science” with Kate Strachnyi (someone who’s also on this list). Follow Jon on LinkedIn 20) Ann K.

BI 52

BI Consulting Data Science Data Governance 52

AltexSoft

MARCH 14, 2023

Built around a cloud data warehouse, data lake, or data lakehouse. Modern data stack tools are designed to integrate seamlessly with cloud data warehouses such as Redshift, Bigquery, and Snowflake, as well as data lakes or even the child of the first two — a data lakehouse.

IT 59

IT Data Warehouse Data Governance Data Lake 59