Knowledge Hut

JUNE 26, 2023

From exploratory data analysis (EDA) and data cleansing to data modeling and visualization, the greatest data engineering projects demonstrate the whole data process from start to finish. Data pipeline best practices should be shown in these initiatives. Source Code: Yelp Review Analysis 2.

Data Engineering 98

Data Engineering Data Engineer Coding Project 98

Towards Data Science

FEBRUARY 6, 2024

Order snapshots are stored in my own development area (image by the author) To prevent my extractions from impacting performance on the operational side, I queried this data regularly and stored it in a persistent staging area (PSA) within my data warehouse.

Systems 81

Systems Raw Data Metadata Data Cleanse 81

Databand.ai

AUGUST 30, 2023

There are various ways to ensure data accuracy. Data validation involves checking data for errors, inconsistencies, and inaccuracies, often using predefined rules or algorithms. Data cleansing involves identifying and correcting errors, inconsistencies, and inaccuracies in data sets.

Data Integration 52

Data Integration Data Cleanse Data Validation Data Governance 52

Databand.ai

AUGUST 30, 2023

This requires implementing robust data integration tools and practices, such as data validation, data cleansing, and metadata management. These practices help ensure that the data being ingested is accurate, complete, and consistent across all sources.

Architecture 52

Architecture Data Ingestion Data Cleanse Data Governance 52

Monte Carlo

MARCH 24, 2023

How Do You Maintain Data Integrity? Data integrity issues can arise at multiple points across the data pipeline. We often refer to these issues as data freshness or stale data. For example: The source system could provide corrupt data or rows with excessive NULLs. What Is Data Validity?

Data Validation 52

Data Validation Data Integration Data Cleanse Data Pipeline 52

Databand.ai

JULY 19, 2023

Enhancing Data Quality Data ingestion plays an instrumental role in enhancing data quality. During the data ingestion process, various validations and checks can be performed to ensure the consistency and accuracy of data. Another way data ingestion enhances data quality is by enabling data transformation.

Data Ingestion 52

Data Ingestion Process Data Cleanse Data Governance 52

Ascend.io

FEBRUARY 28, 2024

Engineers ensure the availability of clean, structured data, a necessity for AI systems to learn from patterns, make accurate predictions, and automate decision-making processes. Through the design and maintenance of efficient data pipelines , data engineers facilitate the seamless flow and accessibility of data for AI processing.

Data Engineering 52

Data Engineering Data Engineer Engineering Metadata 52

Databand.ai

AUGUST 30, 2023

Data profiling: Regularly analyze dataset content to identify inconsistencies or errors. Data cleansing: Implement corrective measures to address identified issues and improve dataset accuracy levels. Automated cleansing tools can correct common errors, such as duplicates or missing values, without manual intervention.

Building 52

Building Data Cleanse Data Governance Datasets 52

Knowledge Hut

JULY 5, 2024

AI-Driven Data Engineering: Overview: Integration of artificial intelligence (AI) into data engineering workflows for enhanced automation and decision-making. Applications: Intelligent data cleansing, predictive data pipeline optimization, and autonomous data quality management.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Cleanse 52

Databand.ai

AUGUST 30, 2023

Techniques The techniques used to maintain data consistency and data integrity also differ: Data consistency is typically maintained through the use of standardized data entry and storage procedures, data synchronization tools, and data cleansing techniques.

Data Integration 52

Data Integration Data Cleanse Data Validation High Quality Data 52

Monte Carlo

OCTOBER 5, 2023

There are several key practices and steps: Before embarking on the ETL process, it’s essential to understand the nature and quality of the source data through data profiling. Data cleansing is the process of identifying and correcting or removing inaccurate records from the dataset, improving the data quality.

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Ascend.io

NOVEMBER 21, 2023

Reduced Complexity: ELT simplifies the data pipeline by eliminating the need for separate ETL servers and staging areas, thus reducing the complexity and maintenance overhead. Read More: Zero ETL: What’s Behind the Hype?

Raw Data 52

Raw Data Data Warehouse Data Cleanse Data Integration 52

DataKitchen

SEPTEMBER 20, 2021

DataOps can and should be implemented in small steps that complement and build upon existing workflows and data pipelines. Lean DataOps relies upon the DataKitchen DataOps Platform , which attaches to your existing data pipelines and toolchains and serves as a process hub.

Data Pipeline 98

Data Pipeline Data Cleanse Process Architecture 98

Monte Carlo

JULY 11, 2023

Regardless of the approach you choose, it’s important to keep a scrutinous eye on whether or not your data outputs are matching (or close to) your expectations; often, relying on a few of these measures will do the trick. When it comes to improving data accuracy, sometimes the simplest – and easiest – way is best.

Data Cleanse 40

Data Cleanse Datasets Data Governance Government 40

Knowledge Hut

APRIL 20, 2023

Data Governance Examples Here are some examples of data governance in practice: Data quality control: Data governance involves implementing processes for ensuring that data is accurate, complete, and consistent. This may involve data validation, data cleansing, and data enrichment activities.

Data Governance 59

Data Governance Government Data Cleanse Data Security 59

Knowledge Hut

NOVEMBER 28, 2023

Technical Data Engineer Skills 1.Python Python Python is one of the most looked upon and popular programming languages, using which data engineers can create integrations, data pipelines, integrations, automation, and data cleansing and analysis.

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

Knowledge Hut

JANUARY 30, 2024

Whether it's aggregating customer interactions, analyzing historical sales trends, or processing real-time sensor data, data extraction initiates the process. Utilizes structured data or datasets that may have already undergone extraction and preparation. Primary Focus Structuring and preparing data for further analysis.

ETL Tools 52

ETL Tools Database-centric Data Mining Raw Data 52

RandomTrees

SEPTEMBER 6, 2020

D-Fast: RandomTrees snowflake accelerator RandomTrees has come up with an accelerator named D-Fast that can help you with fast-track data migration during Snowflake implementation with an approach focused on data quality, cost-effectiveness and business value. The key features include: Rapid migration of data from SAP BW and HANA.

Cloud Storage 52

Cloud Storage Data Ingestion Data Cleanse Data Warehouse 52

Edureka

JULY 5, 2024

It effectively works with Tableau Desktop and Tableau Server to allow users to publish bookmarked, cleaned-up data sources that can be accessed by other personnel within the same organization. This capability underpins sustainable, chattel data cleansing practices requisite to data governance.

Data Preparation 40

Data Preparation Process BI ETL Tools 40

AltexSoft

AUGUST 29, 2023

After residing in the raw zone, data undergoes various transformations. The data cleansing process involves removing or correcting inaccurate records, discrepancies, or inconsistencies in the data. Data enrichment adds value to the original data set by incorporating additional information or context.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 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

Knowledge Hut

MAY 3, 2023

Making sure that data is organized, structured, and available to other teams or apps is the main responsibility of a data engineer. They build data pipelines that transfer data from numerous sources to a single destination, guaranteeing data consistency and quality.

Data Engineering 98

Data Engineering Data Engineer Engineering Data Cleanse 98

Databand.ai

AUGUST 30, 2023

Each type of tool plays a specific role in the DataOps process, helping organizations manage and optimize their data pipelines more effectively. Poor data quality can lead to incorrect or misleading insights, which can have significant consequences for an organization. In this article: Why Are DataOps Tools Important?

Data Cleanse 52

Data Cleanse Data Pipeline Data Ingestion Data Validation 52

Knowledge Hut

FEBRUARY 1, 2024

The data cleaning and validation steps undertaken for any data science project are implemented using a data pipeline. Each stage in a data pipeline consumes input and produces output. The main advantage of the data pipeline is that each step is small, self-contained, and easier to check.

Data Science 52

Data Science Process Data Cleanse Datasets 52

Databand.ai

JULY 19, 2023

Proper Planning and Designing of the Data Pipeline The first step towards successful ELT implementation is proper planning and design of the data pipeline. This involves understanding the business requirements, the source and type of data, the desired output, and the resources required for the ELT process.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Ascend.io

MAY 7, 2024

” Key Partnership Benefits: Cost Optimization and Efficiency : The collaboration is poised to reduce IT and data management costs significantly, including an up to 68% reduction in data stack spend and the ability to build data pipelines 7.5x ABOUT ASCEND.IO

Pipeline-centric 52

Pipeline-centric Data Cleanse Data Security Data Pipeline 52

Towards Data Science

FEBRUARY 10, 2023

As we move firmly into the data cloud era, data leaders need metrics for the robustness and reliability of the machine–the data pipelines, systems, and engineers–just as much as the final (data) product it spits out. What level of data pipeline monitoring coverage do we need?

Data Pipeline 95

Data Pipeline Data Cleanse Machine Learning Data 95

Monte Carlo

FEBRUARY 7, 2023

As we move firmly into the data cloud era, data leaders need metrics for the robustness and reliability of the machine–the data pipelines, systems, and engineers–just as much as the final (data) product it spits out. What level of data pipeline monitoring coverage do we need?

Management 72

Management Data Data Pipeline Data Cleanse 72

Databand.ai

AUGUST 30, 2023

Organizations need to automate various aspects of their data operations, including data integration, data quality, and data analytics. Test and Validate Lastly, organizations need to test and validate their unified DataOps implementation to ensure that it is delivering the desired outcomes.

Data Governance 40

Data Governance Data Cleanse Government Data Pipeline 40

Knowledge Hut

MAY 3, 2024

Spark Streaming Kafka Streams 1 Data received from live input data streams is Divided into Micro-batched for processing. processes per data stream(real real-time) 2 A separate processing Cluster is required No separate processing cluster is required. it's better for functions like row parsing, data cleansing, etc.

Kafka 98

Kafka Scala Java Amazon Web Services 98

Knowledge Hut

JANUARY 18, 2024

Automation and DataOps for Improved Data Analytics Automation and DataOps (Data Operations) are emerging technologies that improve data analytics by streamlining and automating various tasks involved in the data pipeline. Consequently, automation tools reduce manual effort and increase efficiency.

Data Analytics 52

Data Analytics Healthcare Machine Learning Algorithm 52

phData: Data Engineering

AUGUST 4, 2023

Once the data is loaded into Snowflake, it can be further processed and transformed using SQL queries or other tools within the Snowflake environment. This includes tasks such as data cleansing, enrichment, and aggregation. The data can then be processed using Snowflake’s SQL capabilities.

Cloud Storage 52

Cloud Storage Google Cloud Amazon Web Services Data Storage 52

AltexSoft

DECEMBER 23, 2022

Whether your goal is data analytics or machine learning , success relies on what data pipelines you build and how you do it. But even for experienced data engineers, designing a new data pipeline is a unique journey each time. Data engineering in 14 minutes. Aggregation.

Process 52

Process Building Raw Data Data Lake 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. You will analyze accidents happening in NYC.

Data Engineering 52

Data Engineering Data Engineer Coding Project 52

Netflix Tech

MARCH 25, 2019

You are about to make structural changes to the data and want to know who and what downstream to your service will be impacted. Finally, imagine yourself in the role of a data platform reliability engineer tasked with providing advanced lead time to data pipeline (ETL) owners by proactively identifying issues upstream to their ETL jobs.

Building 90

Building Metadata Transportation Data Ingestion 90

AltexSoft

MAY 14, 2021

Modern technologies allow gathering both structured (data that comes in tabular formats mostly) and unstructured data (all sorts of data formats) from an array of sources including websites, mobile applications, databases, flat files, customer relationship management systems (CRMs), IoT sensors, and so on. Data cleansing.

Big Data 78

Big Data Data Analytics IT NoSQL 78

ProjectPro

JANUARY 31, 2023

This process involves learning to understand the data and determining what needs to be done before the data becomes useful in a specific context. Discovery is a big task that may be performed with the help of data visualization tools that help consumers browse their data.

Big Data 52

Big Data Hadoop Relational Database AWS 52