Precisely

JUNE 24, 2024

His key takeaways from the conversation were that “ data leaders are under tremendous pressure to collaborate within the C-Suite on projects that deliver true business value. Explore the key topics and insights from this event below, and get inspired to apply these takeaways for success in your own data-driven journey.

Food 92

Food Data Analytics Pharmaceutical Consulting 92

Workfall

JUNE 12, 2023

It ensures that organisations stay at the forefront by capturing every twist and turn in the data landscape. With CDC by their side, organisations unlock the power of informed decision-making, safeguard data integrity, and enable lightning-fast analytics. CDC also plays a crucial role in data integration and ETL processes.

Telecommunication 80

Telecommunication Metadata Healthcare Finance 80

Data Engineering Podcast

MARCH 22, 2021

What are the factors that you need to consider when deciding whether to implement a CDC system for a given data integration? How does CDC fit into a broader data platform, particularly where there are likely to be other data integration pipelines in operation? What are the barriers to entry?

Data Warehouse 100

Data Warehouse Metadata Hadoop Data Lake 100

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? According to this Spiceworks article, “Interoperability between components and integration readiness are defining traits of this design.”

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Monte Carlo

FEBRUARY 21, 2023

In this post, we’ll discuss what, exactly, a data fabric is, how other companies have used it, and how you can build one at your company. Table of Contents What is a data fabric? According to this Spiceworks article, “Interoperability between components and integration readiness are defining traits of this design.”

Data Architecture 52

Data Architecture Architecture Metadata Unstructured Data 52

Towards Data Science

FEBRUARY 6, 2024

Sales Orders DP exposing sales_orders_dataset (image by the author) The data pipeline in charge of maintaining the data product could be defined like this: Data pipeline steps (image by the author) Data extraction The first step to building source-aligned data products is to extract the data we want to expose from operational sources.

Systems 79

Systems Raw Data Metadata Data Cleanse 79

The Modern Data Company

APRIL 15, 2024

The term “Data Product” has become a buzzword, often misused or overstretched. But at its core, a Data Product is much more than just data. This blend ensures that a Data Product is informative, actionable, and adaptable to various needs.

Government 52

Government Metadata Data Data Lake 52

ProjectPro

JANUARY 24, 2023

You can leverage AWS Glue to discover, transform, and prepare your data for analytics. In addition to databases running on AWS, Glue can automatically find structured and semi-structured data kept in your data lake on Amazon S3, data warehouse on Amazon Redshift, and other storage locations.

AWS 52

AWS Scala Data Lake ETL Tools 52

ProjectPro

FEBRUARY 16, 2023

Data integration with ETL has evolved from structured data stores with high computing costs to natural state storage with read operation alterations thanks to the agility of the cloud. Data integration with ETL has changed in the last three decades.

AWS 52

AWS Data Management ETL Tools Management 52

Ascend.io

MAY 23, 2023

Already in 2016, IBM estimated the cost of bad data to be over three trillion dollars, and that was before the chaos of data lakes emerged and orphaned datasets began to swamp the land. This strategy minimizes clutter and storage space, but could lose some data that is no longer useful going forward.

Datasets 52

Datasets Data Pipeline Metadata Database 52

Databand.ai

AUGUST 30, 2023

In a DataOps architecture, it’s crucial to have an efficient and scalable data ingestion process that can handle data from diverse sources and formats. This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

ProjectPro

JANUARY 24, 2023

AWS Glue based on several aspects to help you choose the right platform for your big data project needs. What is Azure Data Factory? Azure Data Factory is a cloud-based data integration tool that lets you build data-driven processes in the cloud to orchestrate and automate data transfer and transformation.

AWS 52

AWS Cloud Amazon Web Services Scala 52

Silectis

JANUARY 3, 2021

Data Architecture Data architecture is a composition of models, rules, and standards for all data systems and interactions between them. Data Catalog An organized inventory of data assets relying on metadata to help with data management. Database A collection of structured data.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40

AltexSoft

FEBRUARY 11, 2023

It serves as a foundation for the entire data management strategy and consists of multiple components including data pipelines; , on-premises and cloud storage facilities – data lakes , data warehouses , data hubs ;, data streaming and Big Data analytics solutions ( Hadoop , Spark , Kafka , etc.);

Data Architect 87

Data Architect Certification Generalist Big Data 87

Monte Carlo

AUGUST 25, 2023

That’s why it’s essential for teams to choose the right architecture for the storage layer of their data stack. But, the options for data storage are evolving quickly. Different vendors offering data warehouses, data lakes, and now data lakehouses all offer their own distinct advantages and disadvantages for data teams to consider.

Data Lake 52

Data Lake Data Warehouse Unstructured Data Raw Data 52

Monte Carlo

NOVEMBER 14, 2023

Databricks announced that Delta tables metadata will also be compatible with the Iceberg format, and Snowflake has also been moving aggressively to integrate with Iceberg. How Apache Iceberg tables structure metadata. Is your data lake a good fit for Iceberg? Image courtesy of Dremio. Let’s dive in.

Data Lake 64

Data Lake Metadata Data Warehouse SQL 64

ProjectPro

AUGUST 11, 2021

“Data Lake vs Data Warehouse = Load First, Think Later vs Think First, Load Later” The terms data lake and data warehouse are frequently stumbled upon when it comes to storing large volumes of data. Data Warehouse Architecture What is a Data lake?

Data Lake 52

Data Lake Data Warehouse Cloud Hadoop 52

Monte Carlo

MAY 14, 2024

At its core, a table format is a sophisticated metadata layer that defines, organizes, and interprets multiple underlying data files. Table formats incorporate aspects like columns, rows, data types, and relationships, but can also include information about the structure of the data itself.

Data Lake 52

Data Lake Metadata Hadoop Data Governance 52

AltexSoft

OCTOBER 8, 2021

What’s more, that data comes in different forms and its volumes keep growing rapidly every day — hence the name of Big Data. The good news is, businesses can choose the path of data integration to make the most out of the available information. Data integration in a nutshell. Data integration process.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

Data Engineering Podcast

DECEMBER 25, 2022

Atlan is the metadata hub for your data ecosystem. Instead of locking your metadata into a new silo, unleash its transformative potential with Atlan's active metadata capabilities. Missing data? Again, be prepared to have metadata challenges especially. Struggling with broken pipelines? Stale dashboards?

Building 100

Building Metadata Business Intelligence Data Lake 100

ProjectPro

FEBRUARY 8, 2023

Do ETL and data integration activities seem complex to you? Read this blog to understand everything about AWS Glue that makes it one of the most popular data integration solutions in the industry. Did you know the global big data market will likely reach $268.4 Businesses are leveraging big data now more than ever.

AWS 98

AWS Scala Metadata Data Lake 98

Precisely

JANUARY 25, 2024

There are three potential approaches to mainframe modernization: Data Replication creates a duplicate copy of mainframe data in a cloud data warehouse or data lake, enabling high-performance analytics virtually in real time, without negatively impacting mainframe performance. Best Practice 2. Best Practice 3.

Metadata 67

Metadata Relational Database Data Governance Government 67

Databand.ai

JULY 28, 2022

In this very simplified example, we can see an ELT: Some pipeline tasks, probably running by Airflow , are scraping external data sources and collecting data from there. Those tasks are saving the extracted data in the data lake (or warehouse or lakehouse). This technique focuses directly on the data (vs.

Metadata 52

Metadata Data Lake Datasets Data Warehouse 52

Cloudera

MARCH 23, 2022

Figure 1: Apache Iceberg fits the next generation data architecture by abstracting storage layer from analytics layer while introducing net new capabilities like time-travel and partition evolution. #1: Apache Iceberg enables seamless integration between different streaming and processing engines while maintaining data integrity between them.

Metadata 96

Metadata Data Architecture BI Machine Learning 96

Data Engineering Podcast

FEBRUARY 27, 2022

Visit them today at dataengineeringpodcast.com/timescale RudderStack helps you build a customer data platform on your warehouse or data lake. Data integration (extract and load) What are your data sources? What other tools/systems will need to integrate with it? That’s Timescale. That’s Timescale.

Designing 100

Designing Metadata Data Lake Relational Database 100

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

AltexSoft

NOVEMBER 10, 2021

The pun being obvious, there’s more to that than just a new term: Data lakehouses combine the best features of both data lakes and data warehouses and this post will explain this all. What is a data lakehouse? Data warehouse vs data lake vs data lakehouse: What’s the difference.

Architecture 52

Architecture Data Lake Data Warehouse Metadata 52

AltexSoft

NOVEMBER 23, 2021

Not to mention that additional sources are constantly being added through new initiatives like big data analytics , cloud-first, and legacy app modernization. To break data silos and speed up access to all enterprise information, organizations can opt for an advanced data integration technique known as data virtualization.

Process 69

Process Data Lake Metadata Data Warehouse 69

AltexSoft

SEPTEMBER 23, 2021

One of the innovative ways to address this problem is to build a data hub — a platform that unites all your information sources under a single umbrella. This article explains the main concepts of a data hub, its architecture, and how it differs from data warehouses and data lakes. What is Data Hub?

Architecture 52

Architecture Data Lake Unstructured Data Data Warehouse 52

AltexSoft

DECEMBER 23, 2022

Integrating data from numerous, disjointed sources and processing it to provide context provides both opportunities and challenges. One of the ways to overcome challenges and gain more opportunities in terms of data integration is to build an ELT (Extract, Load, Transform) pipeline. ELT process steps. Enrichment.

Process 52

Process Building Raw Data Data Lake 52

AltexSoft

JUNE 26, 2023

Read our article on Hotel Data Management to have a full picture of what information can be collected to boost revenue and customer satisfaction in hospitality. While all three are about data acquisition, they have distinct differences. Data integration , on the other hand, happens later in the data management flow.

Data Collection 52

Data Collection Machine Learning Unstructured Data Non-relational Database 52

AltexSoft

OCTOBER 30, 2021

Managing data and metadata. There are different ways how data can be stored: a data warehouse, numerous data lakes and data hubs , etc. Data engineers control how data is stored and structured within those locations. Providing data access tools. Data warehousing.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Cloudera

JUNE 6, 2019

While this approach provides isolation, it creates another significant challenge: duplication of data, metadata, and security policies, or ‘split-brain’ data lake. Now the admins need to synchronize multiple copies of the data and metadata and ensure that users across the many clusters are not viewing stale information.

Metadata 90

Metadata Data Lake Cloud Big Data 90

AltexSoft

NOVEMBER 7, 2022

Metadata database. A metadata database stores information about user permissions, past and current DAG and task runs, DAG configurations, and more. By default, Airflow handles metadata with SQLite which is meant for development only. The most common applications of the platform are. Airflow still doesn’t have it, though.

PostgreSQL 52

PostgreSQL Metadata Python MySQL 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. However, this approach quickly shows its limitations as data volume escalates. So, why is data orchestration a big deal?

Data Workflow 52

Data Workflow Data Pipeline Data Lake Data 52

AltexSoft

JULY 29, 2022

a runtime environment (sandbox) for classic business intelligence (BI), advanced analysis of large volumes of data, predictive maintenance , and data discovery and exploration; a store for raw data; a tool for large-scale data integration ; and. a suitable technology to implement data lake architecture.

Hadoop 59

Hadoop Big Data Google Cloud NoSQL 59

phData: Data Engineering

SEPTEMBER 27, 2024

Both persistent staging and data lakes involve storing large amounts of raw data. But persistent staging is typically more structured and integrated into your overall customer data pipeline. These changes are streamed into Iceberg tables in your data lake. New user sign-up? Workout completed?

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