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 84

Big Data Metadata Data Warehouse Data 84

Knowledge Hut

JANUARY 30, 2024

This data can be structured, semi-structured, or entirely unstructured, making it a versatile tool for collecting information from various origins. The extracted data is then duplicated or transferred to a designated destination, often a data warehouse optimized for Online Analytical Processing (OLAP).

ETL Tools 52

ETL Tools Database-centric Data Mining Data Cleanse 52

Knowledge Hut

DECEMBER 26, 2023

Certain roles like Data Scientists require a good knowledge of coding compared to other roles. Data Science also requires applying Machine Learning algorithms, which is why some knowledge of programming languages like Python, SQL, R, Java, or C/C++ is also required.

Data Science 97

Data Science BI Business Intelligence Data Mining 97

Knowledge Hut

OCTOBER 31, 2023

The key distinctions between the two jobs are outlined in the following table: Parameter AWS Data Engineer Azure Data Engineer Platform Amazon Web Services (AWS) Microsoft Azure Data Services AWS Glue, Redshift, Kinesis, etc. Azure Data Factory, Databricks, etc.

AWS 52

AWS Data Engineering Data Engineer Engineering 52

Knowledge Hut

NOVEMBER 17, 2023

Here is a step-by-step guide on how to become an Azure Data Engineer: 1. Understanding SQL You must be able to write and optimize SQL queries because you will be dealing with enormous datasets as an Azure Data Engineer. You should possess a strong understanding of data structures and algorithms.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Grouparoo

DECEMBER 14, 2021

ETL, or Extract, Transform, Load, is a process that involves extracting data from different data sources , transforming it into more suitable formats for processing and analytics, and loading it into the target system, usually a data warehouse. ETL data pipelines can be built using a variety of approaches.

Process 52

Process Raw Data Data Warehouse Data Pipeline 52

DareData

JULY 5, 2023

The architecture of a data lake project may contain multiple components, including the Data Lake itself, one or multiple Data Warehouses or one or multiple Data Marts. The Data Lake acts as the central repository for aggregating data from diverse sources in its raw format.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Knowledge Hut

MARCH 13, 2024

Technical expertise: Big data engineers should be thorough in their knowledge of technical fields such as programming languages, such as Java and Python, database management tools like SQL, frameworks like Hadoop, and machine learning. Thus, the role demands prior experience in handling large volumes of data.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

MARCH 13, 2024

Technical expertise Big data engineers should be thorough in their knowledge of technical fields such as programming languages, such as Java and Python, database management tools like SQL, frameworks like Hadoop, and machine learning. Thus, the role demands prior experience in handling large volumes of data.

Big Data 52

Big Data Data Engineering Data Engineer Engineering 52

Knowledge Hut

NOVEMBER 28, 2023

Kafka is great for ETL and provides memory buffers that provide process reliability and resilience. SQL Today, more and more cloud-based systems add SQL-like interfaces that allow you to use SQL. ETL is central to getting your data where you need it.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 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

Grouparoo

FEBRUARY 9, 2022

Today, organizations are adopting modern ETL tools and approaches to gain as many insights as possible from their data. However, to ensure the accuracy and reliability of such insights, effective ETL testing needs to be performed. So what is an ETL tester’s responsibility? Data integration testing.

Process 52

Process ETL System Data Warehouse Metadata 52

dbt Developer Hub

JUNE 12, 2023

Introduction ​ Most data modeling approaches for customer segmentation are based on a wide table with user attributes. This table only stores the current attributes for each user, and is then loaded into the various SaaS platforms via Reverse ETL tools.

Building 59

Building ETL Tools Data Warehouse Machine Learning 59

Knowledge Hut

OCTOBER 3, 2023

Candidates must, however, be proficient in programming concepts and SQL syntax prior to starting the Azure certification training. Additionally, for a job in data engineering, candidates should have actual experience with distributed systems, data pipelines, and related database concepts.

Data Engineering 52

Data Engineering Data Engineer Engineering Cloud Computing 52

AltexSoft

AUGUST 29, 2023

The term data lake itself is metaphorical, evoking an image of a large body of water fed by multiple streams, each bringing new data to be stored and analyzed. Instead of relying on traditional hierarchical structures and predefined schemas, as in the case of data warehouses, a data lake utilizes a flat architecture.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

MAY 2, 2024

As per Apache, “ Apache Spark is a unified analytics engine for large-scale data processing ” Spark is a cluster computing framework, somewhat similar to MapReduce but has a lot more capabilities, features, speed and provides APIs for developers in many languages like Scala, Python, Java and R.

Scala 52

Scala Hospitality Healthcare Retail 52

Knowledge Hut

JANUARY 29, 2024

To excel in this domain, individuals should possess the following skills: Data Transformation: The ability to perform various data transformations, such as merging, ordering, and aggregating, to prepare data for analysis.

Raw Data 52

Raw Data Data Mining Data Preparation Structured Data 52

Monte Carlo

SEPTEMBER 12, 2022

But with the rise of tools such as Segment, Fivetran, Meltano, and Airbyte, it’s become relatively easy for teams to bring all of their data from external sources into a centralized place like a data warehouse. This has led to one emerging role that Maxime believes is here to stay: the analytics engineer.

BI 52

BI Data Governance ETL Tools Data Warehouse 52

Monte Carlo

AUGUST 8, 2023

While this process varies from organization to organization, these unit tests are typically applied by the data engineer after they have built the data pipeline architecture. You should then document this information and even consider creating a data SLA. In these cases it is important to understand data lineage.

Data Validation 52

Data Validation Data Pipeline SQL Data 52

Netflix Tech

JULY 15, 2021

Kevin, what drew you to data engineering? I stumbled into data engineering rather than making an intentional career move into the field. I started my career as an application developer with basic familiarity with SQL. I was later hired into my first purely data gig where I was able to deepen my knowledge of big data.

Data Engineering 75

Data Engineering Data Engineer Engineering Entertainment 75

ProjectPro

DECEMBER 7, 2021

Generally, data pipelines are created to store data in a data warehouse or data lake or provide information directly to the machine learning model development. Keeping data in data warehouses or data lakes helps companies centralize the data for several data-driven initiatives.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

Data Science Blog: Data Engineering

SEPTEMBER 19, 2023

In the contemporary age of Big Data, Data Warehouse Systems and Data Science Analytics Infrastructures have become an essential component for organizations to store, analyze, and make data-driven decisions. So why using IaC for Cloud Data Infrastructures?

Data Warehouse 52

Data Warehouse Coding Systems Cloud 52

ProjectPro

OCTOBER 28, 2015

Some of the common challenges with data ingestion in Hadoop are parallel processing, data quality, machine data on a higher scale of several gigabytes per minute, multiple source ingestion, real-time ingestion and scalability. Sqoop hadoop can also be used for exporting data from HDFS into RDBMS. into HBase, Hive or HDFS.

ETL Tools 52

ETL Tools Hadoop Relational Database Unstructured Data 52

ProjectPro

OCTOBER 15, 2014

Makes use of exact variation of dedicated SQL DDL language by defining tables beforehand. Pig is SQL like but varies to a great extent. Directly leverages SQL and is easy to learn for database experts. Hive Query language (HiveQL) suits the specific demands of analytics meanwhile PIG supports huge data operation.

Hadoop 52

Hadoop Unstructured Data Java SQL 52

Knowledge Hut

MAY 3, 2023

On the other hand, data analysts concentrate on evaluating data to draw conclusions that can be utilized to create data-driven decisions. They gather, purify, and manipulate data before using tools like SQL, Excel, and Tableau to analyze and visualize it.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Cleanse 98

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

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

Monte Carlo

FEBRUARY 15, 2023

On the surface, the promise of scaling storage and processing is readily available for databases hosted on AWS RDS, GCP cloud SQL and Azure to handle these new workloads. Cloud data warehouses solve these problems. What is a data warehouse? Let’s imagine a scenario where you’re collecting orders information.

Data Warehouse 52

Data Warehouse Unstructured Data AWS Business Intelligence 52

Snowflake

FEBRUARY 7, 2023

At best, they can bring in a limited set of properties from an enterprise data warehouse using reverse ETL tools. However, BI tools were not designed for time series and event data analysis, resulting in a mismatch with typical product analytics visualizations like funnels and paths.

BI 78

BI Building ETL Tools Data Warehouse 78

Knowledge Hut

APRIL 19, 2023

It is the process of extracting data from various sources, transforming it into a format suitable for analysis, and loading it into a target database or data warehouse. ETL is used to integrate data from different sources and formats into a single target for analysis. What is an ETL Pipeline?

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

AltexSoft

JANUARY 26, 2022

Data engineers build data pipelines and perform ETL — extract data from sources, transform it, and load it into a centralized repository like a data warehouse. Here’s the video explaining how data engineers work. As the number of technological know-how increases, the data roles get changed and mixed.

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

Knowledge Hut

NOVEMBER 19, 2023

Top 10 Azure Data Engineer Tools I have compiled a list of the most useful Azure Data Engineer Tools here, please find them below. Azure Data Factory Azure Data Factory is a cloud ETL tool for scale-out serverless data integration and data transformation.

Data Engineering 52

Data Engineering Data Engineer Engineering PostgreSQL 52

AltexSoft

OCTOBER 30, 2021

Data engineer’s integral task is building and maintaining data infrastructure — the system managing the flow of data from its source to destination. This typically includes setting up two processes: an ETL pipeline , which moves data, and a data storage (typically, a data warehouse ), where it’s kept.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Science 98

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

Edureka

FEBRUARY 9, 2023

In this blog on “Azure data engineer skills”, you will discover the secrets to success in Azure data engineering with expert tips, tricks, and best practices Furthermore, a solid understanding of big data technologies such as Hadoop, Spark, and SQL Server is required.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Mining 52

Data Engineering Podcast

JANUARY 15, 2022

What are the core principles of data engineering that have remained from the original wave of ETL tools and rigid data warehouses? What are some of the new foundational elements of data products that need to be codified for the next generation of organizations and data professionals?

Engineering 100

Engineering Electronics ETL Tools Data Pipeline 100

Knowledge Hut

DECEMBER 26, 2023

Looker Looker is a data discovery platform entirely browser-based and interacts with any SQL database or data warehouse. Looker also features a proprietary data modeling language called LookML that will simplify life for customers with little to no expertise working with SQL databases.

Business Intelligence 52

Business Intelligence BI Data Mining Data Analysis 52