Rockset

DECEMBER 17, 2020

Overview of the Customer 360 App Our app will make use of real-time data on customer orders and events. We’ll use Rockset to get data from different sources and run analytical queries that power our app in Retool. From there, we’ll create a data API for the SQL query we write in Rockset.

Building 40

Building Aggregated Data SQL Data Ingestion 40

DareData

JANUARY 30, 2023

In this post, we'll discuss some key data engineering concepts that data scientists should be familiar with, in order to be more effective in their roles. These concepts include concepts like data pipelines, data storage and retrieval, data orchestrators or infrastructure-as-code.

Data Engineering 52

Data Engineering Data Engineer NoSQL Engineering 52

ProjectPro

AUGUST 24, 2021

Data Sourcing: Building pipelines to source data from different company data warehouses is fundamental to the responsibilities of a data engineer. So, work on projects that guide you on how to build end-to-end ETL/ELT data pipelines. Google BigQuery receives the structured data from workers.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Rockset

OCTOBER 26, 2022

One was to create another data pipeline that would aggregate data as it was ingested into DynamoDB. And with the NFL season set to start in less than a month, we were in a bind. A Faster, Friendlier Solution We considered a few alternatives. Another was to scrap DynamoDB and find a traditional SQL database.

SQL 52

SQL NoSQL Database Aggregated Data 52

Monte Carlo

FEBRUARY 15, 2023

Second, to reduce your time-to-detection you need to be end-to-end across your entire data system which may include warehouses or lakes from other vendors or other components of the modern data stack. Finally, where and how the data pipeline broke isn’t always obvious. They need to be transformed.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

ProjectPro

JANUARY 25, 2022

Easy Processing- PySpark enables us to process data rapidly, around 100 times quicker in memory and ten times faster on storage. When it comes to data ingestion pipelines, PySpark has a lot of advantages. PySpark allows you to process data from Hadoop HDFS , AWS S3, and various other file systems.

Big Data 52

Big Data Data Process Process Kafka 52

phData: Data Engineering

JANUARY 3, 2022

The role of a data engineer is going to vary depending on the particular needs of your organization. It’s the role of a data engineer to store, extract, transform, load, aggregate, and validate data. This involves: Building data pipelines and efficiently storing data for tools that need to query the data.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Governance 52

ProjectPro

OCTOBER 28, 2015

Apache Hadoop is synonymous with big data for its cost-effectiveness and its attribute of scalability for processing petabytes of data. Data analysis using hadoop is just half the battle won. Getting data into the Hadoop cluster plays a critical role in any big data deployment. then you are on the right page.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

ProjectPro

NOVEMBER 15, 2021

To execute pipelines, beam supports numerous distributed processing back-ends, including Apache Flink, Apache Spark , Apache Samza, Hazelcast Jet, Google Cloud Dataflow, etc. It was built from the ground up for interactive analytics and can scale to the size of Facebook while approaching the speed of commercial data warehouses.

Big Data 52

Big Data Project Metadata Programming Language 52

AltexSoft

MARCH 14, 2023

Joining: combining data from multiple sources based on a common key or attribute. Modeling: transforming the data into a format that is suitable for analysis, including creating data structures, aggregating data, and adding derived fields. Data versioning component in a modern data stack.

IT 59

IT Data Warehouse Data Governance Data Lake 59