Databand.ai

JULY 19, 2023

Whether it is intended for analytics purposes, application development, or machine learning, the aim of data ingestion is to ensure that data is accurate, consistent, and ready to be utilized. It is a crucial step in the data processing pipeline, and without it, we’d be lost in a sea of unusable data.

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Data Ingestion Process Data Cleanse Data Governance 52

Databand.ai

AUGUST 30, 2023

In this article: Why Is Data Testing Important? By identifying bottlenecks, inefficiencies, and performance issues, data testing methods enable businesses to optimize their data systems and applications to deliver optimal performance. These may include data type checks, range and length restrictions, and format validations.

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Grouparoo

OCTOBER 6, 2021

Themes I was drawn to the articles that speak to a theme in the data world that I am passionate about: how data pipelines and data team practices are evolving to be more like traditional product development. 7 Be Intentional About the Batching Model in Your Data Pipelines Different batching models.

Data Engineering 52

Data Engineering Data Engineer Engineering Pipeline-centric 52

Grouparoo

NOVEMBER 29, 2021

The data source is the location of the data that the processing will consume for data processing functions. This can be the point of origin of the data, the place of its creation. Alternatively, this can be data generated by another process and then made available for subsequent processing.

Raw Data 40

Raw Data Big Data Relational Database Data Warehouse 40

Advancing Analytics: Data Engineering

JUNE 21, 2019

Hear me out – back in the on-premises days we had data loading processes that connect directly to our source system databases and perform huge data extract queries as the start of one long, monolithic data pipeline, resulting in our data warehouse. Finally – where we get our data from, is changing massively.

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Data Warehouse Business Intelligence Data Data Validation 52

Precisely

MARCH 14, 2023

Design in the cloud, deploy anywhere – with an array of deployment options for complex data processes. Instead of moving customer data to the processing engine, we move the processing engine to the data. Simply design data pipelines, point them to the cloud environment, and execute.

Data Integration 52

Data Integration High Quality Data BI Data 52

Snowflake

AUGUST 24, 2023

The family of companies, a part of Radian Group, relies on data science and machine learning to build and deploy models using a variety of data from different sources to bring technology to all parts of the home-buyer experience. The team first tried to use a different vendor to store and process billions of rows.

Data Pipeline 52

Data Pipeline Python Programming Language Data Validation 52

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. Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively.

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Databand.ai

JULY 19, 2023

A Beginner’s Guide [SQ] Niv Sluzki July 19, 2023 ELT is a data processing method that involves extracting data from its source, loading it into a database or data warehouse, and then later transforming it into a format that suits business needs. What is ELT (Extract, Load, Transform)? ELT vs. ETL: What Is the Difference?

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ProjectPro

FEBRUARY 16, 2023

Businesses will be better able to make smart decisions and achieve a competitive advantage if they can successfully integrate data from various sources using SQL. This entails managing data access, restricting data movement inside the warehouse, and SQL query optimization strategies. are applied directly to the data in memory.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Databand.ai

AUGUST 30, 2023

A shorter time-to-value indicates that your organization is efficient at processing and analyzing data for decision-making purposes. Monitoring this metric helps identify bottlenecks in the data pipeline and ensures timely insights are available for business users.

Data Cleanse 52

Data Cleanse Metadata High Quality Data Datasets 52

Databand.ai

AUGUST 30, 2023

These experts will need to combine their expertise in data processing, storage, transformation, modeling, visualization, and machine learning algorithms, working together on a unified platform or toolset.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Knowledge Hut

APRIL 19, 2023

Use modular design: Divide the pipeline into small individual parts that can be independently tested and adjusted. Data validation: Data validation as it goes through the pipeline to ensure it meets the necessary quality standards and is appropriate for the final goal.

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

Data Engineering Weekly

FEBRUARY 12, 2023

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. Picnic writes about how it automates pipeline deployment.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Rockset

FEBRUARY 24, 2023

Strong schema support : Avro has a well-defined schema that allows for type safety and strong data validation. Sample use case: Avro is a good choice for big data platforms that need to process and analyze large volumes of log data. This is the single most popular streaming platform.

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Kafka AWS Amazon Web Services Programming Language 52

DataKitchen

SEPTEMBER 21, 2023

L1 is usually the raw, unprocessed data ingested directly from various sources; L2 is an intermediate layer featuring data that has undergone some form of transformation or cleaning; and L3 contains highly processed, optimized, and typically ready for analytics and decision-making processes.

Raw Data 67

Raw Data Data Business Intelligence High Quality Data 67

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.

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Pipeline-centric Database-centric Data Ingestion Data Pipeline 52

DataKitchen

APRIL 13, 2021

Airflow — An open-source platform to programmatically author, schedule, and monitor data pipelines. DBT (Data Build Tool) — A command-line tool that enables data analysts and engineers to transform data in their warehouse more effectively. Reflow — A system for incremental data processing in the cloud.

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Ascend.io

SEPTEMBER 19, 2023

By taking over mundane and repetitive chores (sometimes referred to as “ custodial engineering ”), they free up data engineers to channel their expertise towards more complex, strategic challenges — challenges that require critical thinking, creativity, and domain knowledge.

ETL Tools 52

ETL Tools ETL Method Utilities Data Engineering 52

phData: Data Engineering

JANUARY 3, 2022

How do I know where this data came from or how it’s being used? How do I maintain all my data pipelines? How do I recreate the environment and data sets from scratch? How do I build confidence and trust in the data products I create? How do I ensure customers aren’t impacted by changes or new functionality?

IT 52

IT AWS Software Engineer Software Engineering 52

ProjectPro

JANUARY 31, 2023

Data Storage: The next step after data ingestion is to store it in HDFS or a NoSQL database such as HBase. HBase storage is ideal for random read/write operations, whereas HDFS is designed for sequential processes. Data Processing: This is the final step in deploying a big data model. How to avoid the same.

Big Data 52

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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?

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