ProjectPro

FEBRUARY 8, 2023

Data Integration and Transformation, A good understanding of various data integration and transformation techniques, like normalization, data cleansing, data validation, and data mapping, is necessary to become an ETL developer. Extract, transform, and load data into a target system.

ETL Tools 52

ETL Tools Data Cleanse Data Warehouse Big Data 52

Cloudera

JUNE 16, 2022

Use cases like fraud detection, network threat analysis, manufacturing intelligence, commerce optimization, real-time offers, instantaneous loan approvals, and more are now possible by moving the data processing components up the stream to address these real-time needs. . Convergence of batch and streaming made easy.

Kafka 88

Kafka Manufacturing Data Lake SQL 88

AltexSoft

OCTOBER 30, 2021

But with the start of the 21st century, when data started to become big and create vast opportunities for business discoveries, statisticians were rightfully renamed into data scientists. Data scientists today are business-oriented analysts who know how to shape data into answers, often building complex machine learning models.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

Cloudera

JUNE 26, 2023

Impala only masquerades as an ETL pipeline tool: use NiFi or Airflow instead It is common for Cloudera Data Platform (CDP) users to ‘test’ pipeline development and creation with Impala because it facilitates fast, iterate development and testing. So which open source pipeline tool is better, NiFi or Airflow?

ETL Tools 76

ETL Tools Programming Language Professional Services Datasets 76

Data Engineering Weekly

DECEMBER 18, 2023

.” [link] Netflix: Our First Netflix Data Engineering Summit Netflix publishes the tech talk videos of their internal data summit. It is great to see an internal tech talk with a series focus on data engineering. My highlight is the talk about the data processing pattern around incremental data pipelines.

Data Engineering 68

Data Engineering Data Engineer Engineering Food 68

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

Ascend.io

AUGUST 31, 2023

Performance: Because the data is transformed and normalized before it is loaded , data warehouse engines can leverage the predefined schema structure to tune the use of compute resources with sophisticated indexing functions, and quickly respond to complex analytical queries from business analysts and reports.

Data Lake 52

Data Lake Data Warehouse ETL Tools Data Pipeline 52

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.

Data Engineering 52

Data Engineering Data Engineer AWS Engineering 52

ProjectPro

JANUARY 24, 2023

A survey by Data Warehousing Institute TDWI found that AWS Glue and Azure Data Factory are the most popular cloud ETL tools with 69% and 67% of the survey respondents mentioning that they have been using them. Both AWS Glue and Azure Data Factory can import SSIS packages.

AWS 52

AWS Cloud Amazon Web Services ETL Tools 52

Knowledge Hut

NOVEMBER 17, 2023

cloud Technical Skills for Azure Data Engineers Here I have listed the skills required for an Azure data engineer: 1. Programming and Scripting Languages Proficiency in languages like Python for data manipulation and SQL for database querying, enabling efficient data processing and analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Lake 52

Knowledge Hut

MARCH 28, 2024

Implemented and managed data storage solutions using Azure services like Azure SQL Database , Azure Data Lake Storage, and Azure Cosmos DB. Education & Skills Required Proficiency in SQL, Python, or other programming languages. Implement data ingestion, processing, and analysis pipelines for large-scale data sets.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Warehouse 52

AltexSoft

OCTOBER 21, 2022

The technology was written in Java and Scala in LinkedIn to solve the internal problem of managing continuous data flows. cloud data warehouses — for example, Snowflake , Google BigQuery, and Amazon Redshift. Moving information from database to database has always been the key activity for ETL tools.

Kafka 93

Kafka Hadoop Big Data ETL Tools 93

ProjectPro

FEBRUARY 16, 2023

The process of data extraction from source systems, processing it for data transformation, and then putting it into a target data system is known as ETL, or Extract, Transform, and Load. ETL has typically been carried out utilizing data warehouses and on-premise ETL tools.

AWS 52

AWS Data Management ETL Tools Management 52

Knowledge Hut

NOVEMBER 19, 2023

Azure Data Engineer Tools encompass a set of services and tools within Microsoft Azure designed for data engineers to build, manage, and optimize data pipelines and analytics solutions. These tools help in various stages of data processing, storage, and analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering PostgreSQL 52

DareData

JULY 5, 2023

Learning about general unit testing frameworks such as PyTest or Airflow's testing framework will be very helpful during the development of your ETL processes! Access to Data Lake Storage Either via command line or a SQL interface, it may be beneficial to give your users power to access raw data stored in the lake layer.

Data Lake 52

Data Lake Building Raw Data ETL Tools 52

Edureka

JULY 4, 2024

Databricks runs on an optimized Spark version and gives you the option to select GPU-enabled clusters, making it more suitable for complex data processing. At its core, Azure Synapse combines the power of SQL and Apache Spark technologies. Choose between serverless or dedicated SQL pools for a cost-effective approach.

Data Lake 40

Data Lake Pipeline-centric Data Warehouse ETL Tools 40

Meltano

OCTOBER 5, 2022

DataOps uses a wide range of technologies such as machine learning, artificial intelligence, and various data management tools to streamline data processing, testing, preparing, deploying, and monitoring. 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

Snowflake

JULY 24, 2023

But a mix of legacy technology, plus the costly requirement of maintaining monolithic infrastructure, meant that Fortum’s people were hindered by time-consuming, manual processes, which restricted innovation. Our legacy cluster database, combined with traditional code and ETL tooling, meant our work was inefficient,” said Riipinen.

Engineering 52

Engineering Building Finance Data Lake 52

AltexSoft

MARCH 30, 2023

Source: The Data Team’s Guide to the Databricks Lakehouse Platform Integrating with Apache Spark and other analytics engines, Delta Lake supports both batch and stream data processing. Besides that, it’s fully compatible with various data ingestion and ETL tools. Databricks two-plane infrastructure.

Scala 64

Scala Data Lake Machine Learning BI 64

Towards Data Science

APRIL 6, 2023

And, when it comes to data engineering solutions, it’s no different: They have databases, ETL tools, streaming platforms, and so on — a set of tools that makes our life easier (as long as you pay for them). So, join me on this post to develop a full data pipeline from scratch using some pieces from the AWS toolset.

AWS 98

AWS Data Pipeline Amazon Web Services Python 98

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

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 ought to be able to create a data model that is performance- and scalability-optimized.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Edureka

AUGUST 29, 2024

Design algorithms transforming raw data into actionable information for strategic decisions. Design and maintain pipelines: Bring to life the robust architectures of pipelines with efficient data processing and testing. Databases: Knowledgeable about SQL and NoSQL databases.

Data Engineering 40

Data Engineering Data Engineer Engineering Generalist 40

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 Java Unstructured Data SQL 52

Knowledge Hut

APRIL 19, 2023

Identify source systems and potential problems such as data quality, data volume, or compatibility issues. Step 2: Extract data: extracts the necessary data from the source system. This API may include using SQL queries or other data mining tools. It supports various data sources and formats.

Data Warehouse 52

Data Warehouse Business Intelligence ETL Tools Data Pipeline 52

Edureka

FEBRUARY 7, 2023

Dynamic data masking serves several important functions in data security. It is possible to use Azure SQL Database, Azure SQL Managed Instance and Azure Synapse Analytics. It can be set up as a security policy on all SQL Databases in an Azure subscription. Users can change the level of masking to suit their needs.

Data Engineering 52

Data Engineering Data Engineer Engineering Hadoop 52

Edureka

JULY 4, 2024

Choose Amazon S3 for cost-efficient storage to store and retrieve data from any cluster. It provides an efficient and flexible way to manage the large computing clusters that you need for data processing, balancing volume, cost, and the specific requirements of your big data initiative.

AWS 52

AWS Amazon Web Services Hadoop Big Data 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 is a petabyte-scale service that allows you to analyze all your data using SQL and your favorite business intelligence (BI) tools.

AWS 40

AWS Data Warehouse Amazon Web Services Business Intelligence 40

Monte Carlo

OCTOBER 10, 2024

Machine Learning Basics : Understanding how data impacts model training. Programming Skills : Python, R, and SQL. Attention to Detail : Critical for identifying data anomalies. Tools : Familiarity with data validation tools, data wrangling tools like Pandas , and platforms such as AWS , Google Cloud , or Azure.

Unstructured Data 52

Unstructured Data Google Cloud Machine Learning ETL Tools 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 Machine Learning Healthcare 52

Knowledge Hut

NOVEMBER 28, 2023

Data engineers design, manage, test, maintain, store, and work on the data infrastructure that allows easy access to structured and unstructured data. Data engineers need to work with large amounts of data and maintain the architectures used in various data science projects. Technical Data Engineer Skills 1.Python

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

ProjectPro

JANUARY 19, 2022

Relational and non-relational databases are among the most common data storage methods. Learning SQL is essential to comprehend the database and its structures. ETL (extract, transform, and load) techniques move data from databases and other systems into a single hub, such as a data warehouse.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Storage 52

Edureka

JULY 3, 2024

ADF leverages compute services like Azure HDInsight, Spark, Azure Data Lake Analytics, or Machine Learning to process and analyze the data according to defined requirements. Publish: Transformed data is then published either back to on-premises sources like SQL Server or kept in cloud storage.

Pipeline-centric 52

Pipeline-centric Data Lake Database-centric Data Pipeline 52

ProjectPro

DECEMBER 7, 2021

Big data pipelines must be able to recognize and process data in various formats, including structured, unstructured, and semi-structured, due to the variety of big data. Over the years, companies primarily depended on batch processing to gain insights. However, it is not straightforward to create data pipelines.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 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

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. Learn about well-known ETL tools such as Xplenty, Stitch, Alooma, etc.

Certification 52

Certification Data Engineering Data Engineer Engineering 52