Snowflake

FEBRUARY 7, 2023

Next-gen product analytics is now warehouse-native, an architectural approach that allows for the separation of code and data. In this model, providers of next-gen product analytics maintain code for the analytical application as a connected app, while customers manage the data in their own cloud data platform.

BI 83

BI Building ETL Tools Data Warehouse 83

AltexSoft

OCTOBER 30, 2021

Data engineer’s responsibilities — Development and Architecture. Data engineer’s integral task is building and maintaining data infrastructure — the system managing the flow of data from its source to destination. Engineers can build different types of architectures by mixing and matching these parts.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

ProjectPro

FEBRUARY 16, 2023

With so much riding on the efficiency of ETL processes for data engineering teams, it is essential to take a deep dive into the complex world of ETL on AWS to take your data management to the next level. ETL has typically been carried out utilizing data warehouses and on-premise ETL tools.

AWS 52

AWS Data Management ETL Tools Management 52

Ascend.io

MAY 31, 2023

Data Ingestion Data ingestion is the first step of both ETL and data pipelines. In the ETL world, this is called data extraction, reflecting the initial effort to pull data out of source systems. The data sources themselves are not built to perform analytics.

Data Pipeline 52

Data Pipeline ETL Tools Pipeline-centric Data Warehouse 52

AltexSoft

AUGUST 4, 2021

Now let’s think of sweets as the data required for your company’s daily operations. Instead of combing through the vast amounts of all organizational data stored in a data warehouse, you can use a data mart — a repository that makes specific pieces of data available quickly to any given business unit.

Data Lake 64

Data Lake Data Warehouse ETL Tools Database 64

Ascend.io

DECEMBER 21, 2022

The last three years have seen a remarkable change in data infrastructure. ETL changed towards ELT. Now, data teams are embracing a new approach: reverse ETL. Cloud data warehouses, such as Snowflake and BigQuery, have made it simpler than ever to combine all of your data into one location.

ETL Tools 52

ETL Tools ETL System Data Warehouse Data Consolidation 52

Data Engineering Weekly

DECEMBER 29, 2022

era of Data Catalog Let’s call the pre-modern era; as the state of Data Warehouses before the explosion of big data and subsequent cloud data warehouse adoption. Applications deployed in a large monolithic web server with all the data warehouse changes go through a central data architecture team.

Metadata 62

Metadata Data Warehouse ETL Tools Data Workflow 62

AltexSoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. The number of possible applications tends to grow due to the rise of IoT , Big Data analytics , streaming media, smart manufacturing, predictive maintenance , and other data-intensive technologies.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

Cloudera

NOVEMBER 27, 2023

2: The majority of Flink shops are in earlier phases of maturity We talked to numerous developer teams who had migrated workloads from legacy ETL tools, Kafka streams, Spark streaming, or other tools for the efficiency and speed of Flink. Cloudera Perspective: Deployment architecture matters. Takeaway No. Takeaway No.

Kafka 84

Kafka SQL ETL Tools Architecture 84

Ascend.io

AUGUST 31, 2023

In the dynamic world of data, many professionals are still fixated on traditional patterns of data warehousing and ETL, even while their organizations are migrating to the cloud and adopting cloud-native data services. Modern platforms like Redshift , Snowflake , and BigQuery have elevated the data warehouse model.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

Lyft Engineering

OCTOBER 3, 2023

In this post we will define data quality at a high-level and explore our motivation to achieve better data quality. We will then introduce our in-house product, Verity, and showcase how it serves as a central platform for ensuring data quality in our Hive Data Warehouse. What and Where is Data Quality?

Big Data 90

Big Data Metadata Data Warehouse Data 90

Towards Data Science

NOVEMBER 4, 2023

Often it is a data warehouse solution (DWH) in the central part of our infrastructure. Data warehouse exmaple. It’s worth mentioning that its data frame transformations have been included in one of the basic methods of data loading for many modern data warehouses.

Data Engineering 98

Data Engineering Data Engineer Engineering BI 98

Knowledge Hut

MARCH 28, 2024

They work together with stakeholders to get business requirements and develop scalable and efficient data architectures. Role Level Advanced Responsibilities Design and architect data solutions on Azure, considering factors like scalability, reliability, security, and performance.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

Monte Carlo

SEPTEMBER 8, 2021

Modern data teams have all the right solutions in place to ensure that data is ingested, stored, transformed, and loaded into their data warehouse, but what happens at “the last mile?” In other words, how can data analysts and engineers ensure that transformed, actionable data is actually available to access and use?

ETL Tools 52

ETL Tools Data Warehouse BI Data Pipeline 52

Cloudera

JUNE 16, 2022

This blog aims to answer two questions as illustrated in the diagram below: How have stream processing requirements and use cases evolved as more organizations shift to “streaming first” architectures and attempt to build streaming analytics pipelines? Better yet, it works in any cloud environment.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

Meltano

OCTOBER 5, 2022

Meltano is a DataOps platform that enables data engineers to streamline data management and keep all stages of data production in a single place. Analysis While data engineers don’t typically analyze data, they can prepare the data for analysis for data scientists and business analysts to access and derive insights.

Data Engineering 52

Data Engineering Data Engineer Engineering Raw Data 52

Knowledge Hut

APRIL 19, 2023

In today's data-driven world, businesses need to extract, transform, and load data from multiple data sources because of the large amount of data which businesses generate. An ETL pipeline is one of the most common solutions for the efficient processing of large data. What is an ETL Pipeline?

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

Snowflake

MARCH 30, 2023

A 2016 data science report from data enrichment platform CrowdFlower found that data scientists spend around 80% of their time in data preparation (collecting, cleaning, and organizing of data) before they can even begin to build machine learning (ML) models to deliver business value.

Engineering 105

Engineering Raw Data Data Science Machine Learning 105

AltexSoft

MARCH 30, 2023

It combines the best elements of a data warehouse, a centralized repository for structured data, and a data lake used to host large amounts of raw data. The relatively new storage architecture powering Databricks is called a data lakehouse. Databricks lakehouse platform architecture.

Scala 64

Scala Data Lake Machine Learning BI 64

DareData

JULY 5, 2023

Understanding the Architecture No company is alike and no infrastructure will be alike. Although there are some guidelines that you can follow when setting up a data infrastructure, each company has it's own needs, processes and organizational structure. Data Sources: How different are your data sources?

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Knowledge Hut

DECEMBER 26, 2023

They use tools like Microsoft Power BI or Oracle BI to develop dashboards, reports, and Key Performance Indicator (KPI) scorecards. They should know SQL queries, SQL Server Reporting Services (SSRS), and SQL Server Integration Services (SSIS) and a background in Data Mining and Data Warehouse Design.

Data Science 97

Data Science BI Machine Learning Business Intelligence 97

ProjectPro

SEPTEMBER 30, 2014

If you encounter Big Data on a regular basis, the limitations of the traditional ETL tools in terms of storage, efficiency and cost is likely to force you to learn Hadoop. Systems that contain the data are often not the ones that consume it and Hadoop is changing that concept.

Hadoop 52

Hadoop ETL Tools Unstructured Data ETL System 52

Edureka

JULY 4, 2024

The platform’s massive parallel processing (MPP) architecture empowers you with high-performance querying of even massive datasets. Polyglot Data Processing Synapse speaks your language! This capability fosters a more flexible data architecture where data can be processed and analyzed in its raw form.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

Edureka

AUGUST 29, 2024

The responsibilities of a data engineer imply that the person in this role designs, creates, develops, and maintains systems and architecture that allow them to collect, store, and interpret data. Design algorithms transforming raw data into actionable information for strategic decisions.

Data Engineering 40

Data Engineering Data Engineer Engineering Generalist 40

Knowledge Hut

NOVEMBER 17, 2023

In the post, we will investigate how to become an Azure data engineer, the skills required, the roles and responsibilities of an Azure data engineer, and much more. Who is an Azure Data Engineer? This involves knowing how to manage data partitions, load data into a data warehouse, and speed up query execution.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Meltano

OCTOBER 5, 2022

The responsibilities of a DataOps engineer include: Building and optimizing data pipelines to facilitate the extraction of data from multiple sources and load it into data warehouses. A DataOps engineer must be familiar with extract, load, transform (ELT) and extract, transform, load (ETL) tools.

Engineering 52

Engineering Raw Data Data Pipeline Data Warehouse 52

AltexSoft

OCTOBER 8, 2021

Data integration defines the process of collecting data from a number of disparate source systems and presenting it in a unified form within a centralized location like a data warehouse. So, why is data integration such a big deal? Connections to both data warehouses and data lakes are possible in any case.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

Knowledge Hut

SEPTEMBER 26, 2023

We as Azure Data Engineers should have extensive knowledge of data modelling and ETL (extract, transform, load) procedures in addition to extensive expertise in creating and managing data pipelines, data lakes, and data warehouses. ETL activities are also the responsibility of data engineers.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

Knowledge Hut

NOVEMBER 28, 2023

As a data engineer description, you must be ready to explore large-scale data processing and use your expertise and soft skills to ensure a scalable and reliable working environment. Data engineers need to work with large amounts of data and maintain the architectures used in various data science projects.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Edureka

FEBRUARY 9, 2023

An Azure Data Engineer is a highly qualified expert who is in charge of integrating, transforming, and merging data from various structured and unstructured sources into a structure that can be used to build analytics solutions. Learn about popular ETL tools such as Xplenty, Stitch, Alooma, and others.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

dbt Developer Hub

NOVEMBER 3, 2022

Let’s discuss how to convert events from an event-driven microservice architecture into relational tables in a warehouse like Snowflake. We use Snowflake as our data warehouse where we build dashboards both for internal use and for customers. This data would become the main dbt sources used by our report models in BI.

Kafka 52

Kafka ETL Tools BI Database 52

Edureka

JULY 16, 2024

Introduction Amazon Redshift, a cloud data warehouse service from Amazon Web Services (AWS), will directly query your structured and semi-structured data with SQL. Amazon Redshift Serverless allows customers to analyze and query data without configuring and managing a data warehouse.

AWS 40

AWS Data Warehouse Amazon Web Services Business Intelligence 40

Knowledge Hut

MARCH 13, 2024

While working as a big data engineer, there are some roles and responsibilities one has to do: Designing large data systems starts with designing a capable system that can handle large workloads. Develop the algorithms: Once the database is ready, the next thing is to analyze the data to obtain valuable insights.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 13, 2024

While working as a big data engineer, there are some roles and responsibilities one has to do: Designing large data systems starts with designing a capable system that can handle large workloads. Develop the algorithms: Once the database is ready, the next thing is to analyze the data to obtain valuable insights.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

OCTOBER 3, 2023

The task of integrating, manipulating, and merging data from diverse structured and unstructured sources into a structure utilized to build analytics solutions falls within the purview of an Azure Data Engineer, a highly qualified specialist. How Can I Prepare for the Azure Data Engineer Certification Exam?

Data Engineering 52

Data Engineering Data Engineer Engineering Cloud Computing 52

Edureka

JULY 3, 2024

This integration allows you to version control your data factory resources, automate testing, and deploy changes across different environments with ease. Integrated Security This tool has a bunch of powerful security features seamlessly woven into its architecture. Is Azure Data Factory an ETL tool?

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

ProjectPro

SEPTEMBER 26, 2021

Big Data Engineer performs a multi-faceted role in an organization by identifying, extracting, and delivering the data sets in useful formats. A Big Data Engineer also constructs, tests, and maintains the Big Data architecture. Your organization will use internal and external sources to port the data.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52