Advancing Analytics: Data Engineering

JUNE 10, 2019

Lots of organisations have them, they’re fairly well centralized and controlled – people are terrified of returning to the dark days of data silos and rightly so. However, a data warehouse is a large, sanitary data store. You need data validation & cleansing routines, test coverage, methodical UAT phases and more.

Data Lake 52

Data Lake Data Warehouse Data Data Validation 52

Striim

JANUARY 4, 2023

That’s because you don’t know how many target environments can be used to ingest data from your operational systems. Maybe, you first load data into a data warehouse and later go on to load data into a data lake. Cover schemas in data contracts.

PostgreSQL 52

PostgreSQL Data Warehouse Data Lake Data 52

Data Engineering Podcast

JUNE 16, 2019

Summary Building and maintaining a data lake is a choose your own adventure of tools, services, and evolving best practices. The flexibility and freedom that data lakes provide allows for generating significant value, but it can also lead to anti-patterns and inconsistent quality in your analytics.

Data Lake 100

Data Lake Lambda Architecture Data Warehouse Hadoop 100

Data Engineering Weekly

OCTOBER 30, 2022

link] Dagster: Build a poor man’s data lake from scratch with DuckDB The value of the data is directly proportional to the recency of the data. The modern data stack is trying to address this problem in a silo; the org eventually has to tie everything to make it work.

Data Engineering 52

Data Engineering Data Engineer Engineering Data Ingestion 52

Knowledge Hut

OCTOBER 30, 2023

Data Analysis: Perform basic data analysis and calculations using DAX functions under the guidance of senior team members. Data Integration: Assist in integrating data from multiple sources into Power BI, ensuring data consistency and accuracy. Define data architecture standards and best practices.

BI 52

BI Business Intelligence Data Cleanse Business Analyst 52

Grouparoo

DECEMBER 7, 2021

Transmitting data across multiple paths can identify the compromise of one path or a path exhibiting erroneous behavior and corrupting data. Data validation rules can identify gross errors and inconsistencies within the data set.

Data Integration 52

Data Integration Manufacturing ETL Tools Transportation 52

ProjectPro

FEBRUARY 16, 2023

Data validations or data type checks can be performed using SQL, while duplicates, foreign key constraints, and NULL checks can all be identified using ETL solutions. Data processing tasks containing SQL-based data transformations can be conducted utilizing Hadoop or Spark executors by ETL solutions.

Data Engineering 52

Data Engineering Data Engineer SQL Engineering 52

Databand.ai

JULY 19, 2023

Often, the extraction process includes checks and balances to verify the accuracy and completeness of the extracted data. The Load Phase After the data is extracted, it’s loaded into a data storage system in the load phase. The data is loaded as-is, without any transformation.

Data Cleanse 52

Data Cleanse Raw Data Data Storage Data Warehouse 52

Monte Carlo

MARCH 24, 2023

Most of the large data lakes, data warehouse, or data cloud providers will have tools (usually free) built to help you move to their environment. In fact, given how the cloud and real-time data often go hand in hand, one could even argue it’s more important now than it’s ever been.

Data Warehouse 52

Data Warehouse AWS Cloud Data Governance 52

AltexSoft

NOVEMBER 23, 2021

If the transformation step comes after loading (for example, when data is consolidated in a data lake or a data lakehouse ), the process is known as ELT. You can learn more about how such data pipelines are built in our video about data engineering. Popular data virtualization tools.

Process 69

Process Data Lake Metadata Data Warehouse 69

Ascend.io

JULY 19, 2023

But something about data mesh feels different, doesn’t it? For one, data mesh tackles the real headaches caused by an overburdened data lake and the annoying game of tag that’s too often played between the people who make data, the ones who use it, and everyone else caught in the middle.

Data Governance 40

Data Governance Government Metadata Data 40

phData: Data Engineering

JANUARY 3, 2022

One of our customers needed the ability to export/import data between systems and create data products from this source data. This required applying transformations and filters to the data for various business units. The data was being stored in their data lake (AWS S3) and within their data warehouse (AWS Redshift).

IT 52

IT AWS Software Engineer Software Engineering 52

ProjectPro

NOVEMBER 30, 2021

The data sources can be an RDBMS or some file formats like XLSX, CSV, JSON, etc., We need to extract data from all the sources and convert it into a single format for standardized processing. Validate data: Validating the data after extraction is essential to ensure it matches the expected range and rejects it if it does not.

Process 52

Process Data Pipeline Data Warehouse AWS 52

ProjectPro

JANUARY 31, 2023

Step 4: Data Transformation and Enrichment Data transformation involves changing the format or value inputs to achieve a specific result or to make the data more understandable to a larger audience. Enriching data entails connecting it to other related data to produce deeper insights.

Big Data 52

Big Data Hadoop AWS Relational Database 52

ProjectPro

AUGUST 21, 2015

We can use python to read the incoming data into pandas data frames and perform various checks on the data and transform the data into the required format, clean the data, validate the data, and store it into the data lake or HDFS for further processing 2) What are the challenges that you encounter when testing large datasets?

Hadoop 40

Hadoop Big Data Metadata Java 40