Databand.ai

AUGUST 30, 2023

Data profiling tools: Profiling plays a crucial role in understanding your dataset’s structure and content. This is part of a series of articles about data quality. Improved data quality The primary goal of using data testing tools is to enhance the overall quality of an organization’s data assets.

Data Cleanse 52

Data Cleanse Data Pipeline Datasets Data Validation 52

Towards Data Science

NOVEMBER 4, 2023

Tools like Databricks, Tabular and Galaxy try to solve this problem and it really feels like the future. Indeed, datalakes can store all types of data including unstructured ones and we still need to be able to analyse these datasets. You can change these # to conform to your data. Datalake example. Image by author.

Data Engineering 82

Data Engineering Data Engineer Engineering BI 82

Monte Carlo

OCTOBER 5, 2023

Data cleansing is the process of identifying and correcting or removing inaccurate records from the dataset, improving the data quality. This step might involve removing duplicate data, correcting typos and inaccuracies, and filling in missing values. Ready to leap to the next level of data management prowess?

Data Cleanse 52

Data Cleanse ETL Tools Datasets High Quality Data 52

Ascend.io

SEPTEMBER 19, 2023

Equipped with built-in connectors, robust error-handling mechanisms, and capabilities to process data in near real-time, automated ETL pipelines not only enhance the speed of data integration but also ensure a higher degree of accuracy and reliability. A more agile, responsive, and error-resistant data management process.

ETL Tools 52

ETL Tools ETL Method Utilities Data Engineering 52

Knowledge Hut

NOVEMBER 3, 2023

The need for efficient and agile data management products is higher than ever before, given the ongoing landscape of data science changes. MongoDB is a NoSQL database that’s been making rounds in the data science community. js combined with MongoDB for advanced data visualizations.

MongoDB 52

MongoDB Data Science NoSQL ETL Tools 52

Silectis

FEBRUARY 25, 2021

Assessing the value of lineage for your team requires defining its scope, understanding the specific benefits it delivers, and putting it in context within the broader architecture of tools used for data management. This makes lineage a useful tool in analysis, but also in identifying and diagnosing issues.

ETL Tools 52

ETL Tools Metadata Data Data Engineering 52

Databand.ai

JULY 5, 2023

Improved Collaboration Among Teams Data engineering teams frequently collaborate with other departments, such as analysts or scientists, who depend on accurate datasets for their tasks. Boosting Operational Efficiency A well-monitored data pipeline can significantly increase an organization’s operational efficiency.

Data Pipeline 52

Data Pipeline Data Lake Data Warehouse Datasets 52

Knowledge Hut

NOVEMBER 17, 2023

The data engineers are responsible for creating conversational chatbots with the Azure Bot Service and automating metric calculations using the Azure Metrics Advisor. Data engineers must know data management fundamentals, programming languages like Python and Java, cloud computing and have practical knowledge on data technology.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

Knowledge Hut

MAY 9, 2024

Data Architect ScyllaDB Data architects play a crucial role in designing an organization's data management framework by assessing data sources and integrating them into a centralized plan. Average Annual Salary of Big Data Engineer A big data engineer makes around $120,269 per year.

Data Science 52

Data Science Data Mining Data Architect Programming Language 52

AltexSoft

OCTOBER 30, 2021

Regardless of the structure they eventually build, it’s usually composed of two types of specialists: builders, who use data in production, and analysts, who know how to make sense of data. Distinction between data scientists and engineers is similar. Data scientist’s responsibilities — Datasets and Models.

Data Engineering 98

Data Engineering Data Engineer Engineering Machine Learning 98

AltexSoft

AUGUST 29, 2023

If your organization fits into one of these categories and you’re considering implementing advanced data management and analytics solutions, keep reading to learn how data lakes work and how they can benefit your business. For example, it might be set to run nightly or weekly, transferring large chunks of data at a time.

Data Lake 52

Data Lake Architecture IT Amazon Web Services 52

Knowledge Hut

MARCH 22, 2024

Education & Skills Required Bachelor’s or Master’s degree in Computer Science, Data Science , or a related field. Good Hold on MongoDB and data modeling. Experience with ETL tools and data integration techniques. Strong programming skills (e.g., Python, Java).

MongoDB 52

MongoDB Amazon Web Services Computer Science Education 52

phData: Data Engineering

DECEMBER 16, 2021

Listed below are some of the interesting features we found: Query Log Ingestion and the Behavioral Analysis Engine (BAE) Alation has a tool that will ingest and parse queries. You can determine which datasets are popular and might be a good candidate to transition to the data catalog right away.

Metadata 52

Metadata Datasets ETL Tools Cloud 52

Meltano

OCTOBER 5, 2022

For this purpose, various parts of the data pipeline are automated to deliver analytics quickly and efficiently. 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.

Engineering 52

Engineering Raw Data Data Pipeline ETL Tools 52

ProjectPro

JANUARY 19, 2022

The use of data has risen significantly in recent years. More people, organizations, corporations, and other entities use data daily. Earlier, people focused more on meaningful insights and analysis but realized that data management is just as important. Different methods are used to store different types of data.

Data Engineering 52

Data Engineering Data Engineer Engineering Scala 52

ProjectPro

NOVEMBER 30, 2021

The AWS Glue Data Catalog automatically loads your data and the associated metadata. Your data will be immediately accessible and available for the ETL data pipeline once this process is over. Talend One of the most significant data integration ETL tools in the market is Talend Open Studio (TOS).

Process 52

Process Data Pipeline Data Warehouse AWS 52

AltexSoft

JANUARY 26, 2022

For more detailed information on data science team roles, check our video. An analytics engineer is a modern data team member that is responsible for modeling data to provide clean, accurate datasets so that different users within the company can work with them. Data modeling. What is an analytics engineer?

Engineering 59

Engineering Software Engineer Software Engineering Data Warehouse 59

AltexSoft

MARCH 30, 2023

What is Databricks Databricks is an analytics platform with a unified set of tools for data engineering, data management , data science, and machine learning. 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.

Scala 64

Scala Data Lake BI Google Cloud 64

Knowledge Hut

DECEMBER 26, 2023

Data storage for business intelligence You'll typically need three levels of accessible data storage for your business intelligence solutions: primary data storage, data warehouse/historical storage, and analytical databases. You will also need an ETL tool to transport data between each tier.

Business Intelligence 52

Business Intelligence BI Data Mining Data Analysis 52

Knowledge Hut

NOVEMBER 28, 2023

As a Data Engineer, you must: Work with the uninterrupted flow of data between your server and your application. Work closely with software engineers and data scientists. Data Visualization To successfully fulfill ETL or ELT-related work, you must be well-versed in exploratory data analysis (EDA).

Data Engineering 52

Data Engineering Data Engineer Engineering Generalist 52

AltexSoft

OCTOBER 8, 2021

The bad news is, integrating data can become a tedious task, especially when done manually. Luckily, there are various data integration tools that support automation and provide a unified data view for more efficient data management. Data integration process. Data loading. Data loading.

Data Integration 52

Data Integration Hadoop Data Warehouse Data Lake 52

ProjectPro

DECEMBER 7, 2021

A pipeline may include filtering, normalizing, and data consolidation to provide desired data. It can also consist of simple or advanced processes like ETL (Extract, Transform and Load) or handle training datasets in machine learning applications.

Data Pipeline 52

Data Pipeline Architecture Kafka AWS 52

ProjectPro

SEPTEMBER 14, 2016

Get FREE Access to Data Analytics Example Codes for Data Cleaning, Data Munging, and Data Visualization What does a Hadoop developer do on a daily basis? Loading data from different datasets and deciding on which file format is efficient for a task. Managing Hadoop jobs using scheduler.

Hadoop 40

Hadoop Unstructured Data Java Big Data 40

ProjectPro

FEBRUARY 21, 2023

Due to the enormous amount of data being generated and used in recent years, there is a high demand for data professionals, such as data engineers, who can perform tasks such as data management, data analysis, data preparation, etc. big data and ETL tools, etc.

Certification 52

Certification Data Engineering Data Engineer Engineering 52

ProjectPro

MARCH 21, 2016

There are many solutions from vendors like Syncsort, Veristorm, Compuware and BMC that target mainframe data with enhanced Hadoop ETL tools. Organizations should begin with creating copies of selected mainframe datasets in HDFS and then migrating huge volumes of data from various semi-structured sources and RDBMs.

Hadoop 40

Hadoop Insurance Big Data Retail 40

ProjectPro

JUNE 2, 2015

Business win online when they use hard-to-copy technology to deliver a superior customer experience through mining larger and larger datasets.”- It is estimated that a data analyst spends close to 80% of the time in cleaning and preparing the big data for analysis whilst only 20% is actually spent on analysis work.

Big Data 40

Big Data Hadoop Banking Retail 40

ProjectPro

JULY 27, 2021

Non-relational databases are ideal if you need flexibility for storing the data since you cannot create documents without having a fixed schema. Since non-RDBMS are horizontally scalable, they can become more powerful and suitable for large or constantly changing datasets. E.g. PostgreSQL, MySQL, Oracle, Microsoft SQL Server.

Data Engineering 40

Data Engineering Data Engineer Engineering Hadoop 40