Towards Data Science

JULY 8, 2024

Leveraging TensorFlow Transform for scaling data pipelines for production environments Photo by Suzanne D. Williams on Unsplash Data pre-processing is one of the major steps in any Machine Learning pipeline. This process also creates a sqlite database for storing the metadata of the pipeline process.

Data Preparation 70

Data Preparation Datasets Metadata Data Ingestion 70

Grouparoo

OCTOBER 6, 2021

This provided a nice overview of the breadth of topics that are relevant to data engineering including data warehouses/lakes, pipelines, metadata, security, compliance, quality, and working with other teams. For example, grouping the ones about metadata, discoverability, and column naming might have made a lot of sense.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Databand.ai

AUGUST 30, 2023

Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively. Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. In this article: Why Are DataOps Tools Important?

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Wayne Yaddow

MARCH 14, 2025

How dbt Core aids data teams test, validate, and monitor complex data transformations and conversions Photo by NASA on Unsplash Introduction dbt Core, an open-source framework for developing, testing, and documenting SQL-based data transformations, has become a must-have tool for modern data teams as the complexity of data pipelines grows.

Unstructured Data 52

Unstructured Data SQL Data Pipeline Data Validation 52

Ascend.io

AUGUST 31, 2023

In this article, we assess: The role of the data warehouse on one hand, and the data lake on the other; The features of ETL and ELT in these two architectures; The evolution to EtLT; The emerging role of data pipelines. Let’s take a closer look.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. The essential components of the virtual layer are.

Process 69

Process Data Lake Metadata Data Warehouse 69

Monte Carlo

OCTOBER 2, 2024

Here is a list of the most popular tools for data lineage in Python: OpenLineage and Marquez : OpenLineage is an open framework for data lineage collection and analysis. Marquez is a metadata service that implements the OpenLineage API.

Python 52

Python Metadata Datasets Data 52

Data Engineering Weekly

OCTOBER 30, 2022

Data Engineering Weekly Is Brought to You by RudderStack RudderStack provides data pipelines that make it easy to collect data from every application, website, and SaaS platform, then activate it in your warehouse and business tools. Sign up free to test out the tool today.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

RandomTrees

SEPTEMBER 6, 2020

The architecture is three layered: Database Storage: Snowflake has a mechanism to reorganize the data into its internal optimized, compressed and columnar format and stores this optimized data in cloud storage. This stage handles all the aspects of data storage like organization, file size, structure, compression, metadata, statistics.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Databand.ai

AUGUST 30, 2023

This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management. These practices help ensure that the data being ingested is accurate, complete, and consistent across all sources.

Architecture 52

Architecture Data Ingestion Data Governance Data Cleanse 52

Data Engineering Weekly

MARCH 10, 2024

Pradheep Arjunan - Shared insights on AZ's journey from on-prem to the cloud data warehouses. Google: Croissant- a metadata format for ML-ready datasets Google Research introduced Croissant, a new metadata format designed to make datasets ML-ready by standardizing the format, facilitating easier use in machine learning projects.

Data Engineering 52

Data Engineering Data Engineer Engineering Datasets 52

Monte Carlo

MARCH 12, 2024

With Dataplex, teams get lineage and visibility into their data management no matter where it’s housed, centralizing the security, governance, search and discovery across potentially distributed systems. Dataplex works with your metadata. The SQL expression should evaluate to true (pass) or false (fail) per row.

Google Cloud 52

Google Cloud Metadata SQL Data Lake 52

Ascend.io

JUNE 8, 2023

The Essential Six Capabilities To set the stage for impactful and trustworthy data products in your organization, you need to invest in six foundational capabilities. Data pipelines Data integrity Data lineage Data stewardship Data catalog Data product costing Let’s review each one in detail.

Pipeline-centric 52

Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

Databand.ai

NOVEMBER 4, 2022

Re-Imagining Data Observability Ryan Yackel 2022-11-04 10:36:35 Data observability has become one of the hottest topics of the year – and for good reason. Data observability provides an end-to-end view into exactly what’s happening with data pipelines across an organization’s data fabric.

Data 52

Data Data Pipeline Retail Metadata 52

Striim

JANUARY 4, 2023

A data contract is a formal agreement between the users of a source system and the data engineering team that is extracting data for a data pipeline. This data is loaded into a data repository — such as a data warehouse — where it can be transformed for end users. temperature).

PostgreSQL 52

PostgreSQL Data Warehouse Data Data Lake 52

DataKitchen

SEPTEMBER 21, 2023

This results in rallying 26 team members—likely the cream of the crop—to spend an entire day investigating the problem, only to discover that a single blank field passed through the data pipeline was the culprit. This existing paradigm fails to address the challenges and intricacies of “Data in Use.”

Raw Data 69

Raw Data Data Business Intelligence Data Engineering 69

Databand.ai

AUGUST 30, 2023

This includes defining roles and responsibilities related to managing datasets and setting guidelines for metadata management. Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Data cleansing: Implement corrective measures to address identified issues and improve dataset accuracy levels.

Building 52

Building Data Cleanse Data Governance Datasets 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. Organizations need to automate various aspects of their data operations, including data integration, data quality, and data analytics.

Data Governance 40

Data Governance Data Cleanse Government Data Science 40

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. Contextual understanding: Data quality is also influenced by the availability of relevant contextual information.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Knowledge Hut

APRIL 20, 2023

Data Governance Examples Here are some examples of data governance in practice: Data quality control: Data governance involves implementing processes for ensuring that data is accurate, complete, and consistent. This may involve data validation, data cleansing, and data enrichment activities.

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

Monte Carlo

JANUARY 25, 2023

All of these options allow you to define the schema of the contract, describe the data, and store relevant metadata like semantics, ownership, and constraints. We can specify the fields of the contract in addition to metadata like ownership, SLA, and where the table is located. Consistency in your tech stack.

Data Warehouse 52

Data Warehouse Data High Quality Data Metadata 52

ProjectPro

JANUARY 31, 2023

Why is HDFS only suitable for large data sets and not the correct tool for many small files? NameNode is often given a large space to contain metadata for large-scale files. The metadata should come from a single file for optimal space use and economic benefit. And storing these metadata in RAM will become problematic.

Big Data 52

Big Data Hadoop Relational Database AWS 52

ProjectPro

NOVEMBER 30, 2021

This guide provides definitions, a step-by-step tutorial, and a few best practices to help you understand ETL pipelines and how they differ from data pipelines. The crux of all data-driven solutions or business decision-making lies in how well the respective businesses collect, transform, and store data.

Process 52

Process Data Warehouse Data Pipeline AWS 52

DataKitchen

JANUARY 23, 2023

It allows organizations to see how data is being used, where it is coming from, its quality, and how it is being transformed. DataOps Observability includes monitoring and testing the data pipeline, data quality, data testing, and alerting. What is missing in data lineage?

Data Governance 52

Data Governance Government Data Pipeline Data 52