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• big data engineer

  A big data engineer is an information technology (IT) professional who is responsible for designing, building, testing and maintaining complex data processing systems that work with large data sets. Continue Reading

• NoSQL (Not Only SQL database)

  NoSQL is an approach to database management that can accommodate a wide variety of data models, including key-value, document, columnar and graph formats. Continue Reading

• How parallelization works in streaming systems

  Dive into this book excerpt from 'Grokking Streaming Systems' and learn the crucial role the parallelization process plays in the design of a streaming system. Continue Reading

• How a DataOps pipeline can support your data

  DataOps has created a lot of hype as a data management pipeline because of its focus on collaboration and flexibility. Read on to find out how these priorities support your data. Continue Reading

• 5 V's of big data

  The 5 V's of big data are the five main and innate characteristics of big data. Continue Reading

• Definitions to Get Started

  • big data engineer
  • NoSQL (Not Only SQL database)
  • 5 V's of big data
  • data engineer

  • Hadoop Distributed File System (HDFS)
  • feature engineering
  • Google BigQuery
  • data analytics (DA)

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• data engineer

  A data engineer is an IT worker whose primary job is to prepare data for analytical or operational uses.Continue Reading

• What is a data warehouse analyst?

  Data warehouses aren't a new part of the data pipeline, but analyst roles to manage the repository are increasing. Here's a look at the role and responsibilities.Continue Reading

• Hadoop Distributed File System (HDFS)

  The Hadoop Distributed File System (HDFS) is the primary data storage system used by Hadoop applications.Continue Reading

• Open source database migration guide: How to transition

  Open source database transitions have been on the rise as they prove to be worthy competitors to commercial database options, but that transition requires strategy and user buy-in.Continue Reading

• Why your data story matters and how to tell it

  Data storytelling isn't just for business analysts. Find out how to build a data management story and why you need to have one in the first place.Continue Reading

• Creating a data advantage by building a data ecosystem

  Developing a data ecosystem will improve personalization and customer retention. Find out how data mining across channels can build a data advantage for your organization.Continue Reading

• Enterprise data lakes hold the key to actionable insights

  Technological pillars of sound business decisions, AI, machine learning and advanced analytics depend on the quantity, quality and integrity of information in data lakes.Continue Reading

• Graph database vs. relational database: Key differences

  Relational databases and graph databases both focus on the relationships between data but not in the same ways. Here are some key differences between the two.Continue Reading

• feature engineering

  Feature engineering is the process that takes raw data and transforms it into features that can be used to create a predictive model using machine learning or statistical modeling, such as deep learning.Continue Reading

• DBMS keys: Primary, super, foreign and candidate keys with examples

  Here's a guide to primary, super, foreign and candidate keys, what they're used for in relational database management systems and the differences among them.Continue Reading

• Top 5 U.S. open data use cases from federal data sets

  The U.S. government has made data sets from many federal agencies available for public access to use and analyze. Check out some of the ways that data is being used.Continue Reading

• Quiz on MongoDB 4 new features and database updates

  Check out this excerpt from the new book Learn MongoDB 4.x from Packt Publishing, then quiz yourself on new updates and features to the database.Continue Reading

• Google BigQuery

  Google BigQuery is a cloud-based big data analytics web service for processing very large read-only data sets.Continue Reading

• Why understanding data structures is so important to coders

  Jay Wengrow talks about how his new book on data structures and algorithms and considerations for making your choices as efficient as possible.Continue Reading

• Key steps in the feature engineering process

  Feature engineering is key to machine learning algorithms. Read on to learn how those features are created and chosen to increase the accuracy of those models.Continue Reading

• data analytics (DA)

  Data analytics (DA) is the process of examining data sets in order to find trends and draw conclusions about the information they contain.Continue Reading

• Apache Hadoop YARN

  Apache Hadoop YARN is the resource management and job scheduling technology in the open source Hadoop distributed processing framework.Continue Reading

• MongoDB

  MongoDB is an open source NoSQL database management program. NoSQL is used as an alternative to traditional relational databases.Continue Reading

• How to ensure your data lake security

  Your data lake is full of sensitive information and securing that data is a top priority. These are the best practices to keep that information safe from hackers.Continue Reading

• When a DIY database management system design is the best fit

  Learn how a combination of homegrown, off-the-shelf and open source tools, plus proper motivation, can yield a DIY DBMS that meets corporate expectations, needs and ROI.Continue Reading

• Building a database application the DIY way

  Business users experience the trials, tribulations and exultations of building a DIY DBMS, especially when IT expertise is not readily available or costs are too high.Continue Reading

• relational database

  A relational database is a collection of information that organizes data points with defined relationships for easy access.Continue Reading

• Developing an enterprise data strategy: 10 steps to take

  Consultants detail 10 to-do items for data management teams looking to create a data strategy to help their organization use data more effectively in business operations.Continue Reading

• database replication

  Database replication is the frequent electronic copying of data from a database in one computer or server to a database in another -- so that all users share the same level of information.Continue Reading

• What is data governance and why does it matter?

  Data governance (DG) is the process of managing the availability, usability, integrity and security of the data in enterprise systems, based on internal data standards and policies that also control data usage.Continue Reading

• What steps are key to building a data catalog?

  An enterprise data catalog can help data stewards and other users in an organization manage metadata and explore data assets. Here are 10 key steps for creating a data catalog.Continue Reading

• data stewardship

  Data stewardship is the management and oversight of an organization's data assets to help provide business users with high-quality data that is easily accessible in a consistent manner.Continue Reading

• consumer privacy (customer privacy)

  Consumer privacy, also known as customer privacy, involves the handling and protection of the sensitive personal information provided by customers in the course of everyday transactions.Continue Reading

• corporate performance management (CPM)

  Corporate performance management (CPM) is a term used to describe the various processes and methodologies involved in aligning an organization's strategies and goals to its plans and executions in order to control the success of the company.Continue Reading

• Extract, Load, Transform (ELT)

  Extract, Load, Transform (ELT) is a data integration process for transferring raw data from a source server to a data system (such as a data warehouse or data lake) on a target server and then preparing the information for downstream uses.Continue Reading

• How data lineage tools boost data governance policies

  Organizations can bolster data governance efforts by tracking the lineage of data in their systems. Get advice on how to do so and key features in data lineage tools.Continue Reading

• Data warehousing design and value change with the times

  Big data, the cloud and analytics profoundly shape data warehouse purpose and design. Learn how companies derive value from a repository that at times needs definition.Continue Reading

• database as a service (DBaaS)

  Database as a service (DBaaS) is a cloud computing managed service offering that provides access to a database without requiring the setup of physical hardware, the installation of software or the need to configure the database.Continue Reading

• data integration

  Data integration is the process of combining data from multiple source systems to create unified sets of information for both operational and analytical uses.Continue Reading

• RDBMS (relational database management system)

  A relational database management system (RDBMS) is a collection of programs and capabilities that enable IT teams and others to create, update, administer and otherwise interact with a relational database.Continue Reading

• master data management (MDM)

  Master data management (MDM) is a process that creates a uniform set of data on customers, products, suppliers and other business entities from different IT systems.Continue Reading

• data quality

  Data quality is a measure of the condition of data based on factors such as accuracy, completeness, consistency, reliability and whether it's up to date.Continue Reading

• data classification

  Data classification is the process of organizing data into categories that make it is easy to retrieve, sort and store for future use.Continue Reading

• What is data management and why is it important?

  Data management is the process of ingesting, storing, organizing and maintaining the data created and collected by an organization, as explained in this in-depth look at the process.Continue Reading

• big data

  Big data is a combination of structured, semistructured and unstructured data collected by organizations that can be mined for information and used in machine learning projects, predictive modeling and other advanced analytics applications.Continue Reading

• Hadoop

  Hadoop is an open source distributed processing framework that manages data processing and storage for big data applications running in clustered systems.Continue Reading

• Third-party database tools boast attractive alternatives

  For companies considering third-party database tools, this handbook provides expert advice on evaluating and deploying on-premises and cloud options from third parties.Continue Reading

• Entity Relationship Diagram (ERD)

  An entity relationship diagram (ERD), also known as an entity relationship model, is a graphical representation that depicts relationships among people, objects, places, concepts or events within an information technology (IT) system.Continue Reading

• data

  In computing, data is information that has been translated into a form that is efficient for movement or processing.Continue Reading

• data warehouse

  A data warehouse is a federated repository for all the data collected by an enterprise's various operational systems, be they physical or logical.Continue Reading

• Big data containers gain wider appeal in system deployments

  This handbook examines the use of Docker containers in Kubernetes clusters to run big data systems and offers insight on container deployment and management issues.Continue Reading

• Data virtualization tools promote anywhere, anytime data access

  This online handbook examines data virtualization software and how organizations are deploying and using the technology as part of their data integration processes.Continue Reading

• Data as a Service (DaaS)

  Data as a Service (DaaS) is an information provision and distribution model in which data files (including text, images, sounds, and videos) are made available to customers over a network, typically the Internet.Continue Reading

• Advice on enterprise data cleansing from an SAP VP

  SAP's Kristin McMahon details data cleansing best practices and explains why a good data cleanse needs continual communication, collaboration and oversight.Continue Reading

• DataOps (data operations)

  DataOps (data operations) is an Agile approach to designing, implementing and maintaining a distributed data architecture that will support a wide range of open source tools and frameworks in production. The goal of DataOps is to create business ...Continue Reading

• data virtualization

  Data virtualization is an umbrella term used to describe any approach to data management that allows an application to retrieve and manipulate data without needing to know any technical details about the data such as how it is formatted or where it ...Continue Reading

• Data model design tips to help standardize business data

  Data models should be understandable to business users and kept to a reasonable scope, say the leaders of a data modeling initiative at England's Environment Agency.Continue Reading

• USA Patriot Act

  The USA Patriot Act is a law enacted in 2001, granting new and extended data-collection abilities to federal agencies in an effort to combat terrorism after the September 11 attacks.Continue Reading

• USAA adds data engineering skills to speed data science work

  When the United Services Automobile Association's data science team wasn't getting data in the right format, the team lead realized the USAA needed more data engineers.Continue Reading

• data profiling

  Data profiling is the process of examining, analyzing and reviewing data to collect statistics surrounding the quality and hygiene of the dataset.Continue Reading

• 5 things to know about deploying big data systems in data containers

  Planning for security and container APIs, and watching out for infrastructure sprawls are some issues to be aware of before deploying big data in containers.Continue Reading

• DataOps is more than DevOps for data, Delphix CTO says

  Data operations is young compared to DevOps, but it is increasingly used as part of projects that put data at the center of development. Here, Delphix CTO Eric Schrock makes observations about the trend.Continue Reading

• HR makes major strides toward improving employee engagement

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• What is the difference between DBMS and RDBMS?

  A relational database management system is the most popular type of database management system for business uses. Find out how RDBMS software differs from DBMS technology in general.Continue Reading

• data modeling

  Data modeling is the process of documenting a complex software system design as an easily understood diagram, using text and symbols to represent the way data needs to flow.Continue Reading

• SQL vs. NoSQL: What do you know about the database designs?

  The decision to use a SQL database or a NoSQL database can be made wisely only if the ins and outs of both are understood. See how well you know the database architectures.Continue Reading

• data mart (datamart)

  A data mart is a repository of data that is designed to serve a particular community of knowledge workers.Continue Reading

• 11 features to look for in data quality management tools

  As the need for quality data has increased, so have the capabilities of data quality tools. Learn how collaboration, data lineage and other features enable data quality.Continue Reading

• AI for analytics augments and bolsters business intelligence

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• What is an enterprise data strategy?

  Defining a data strategy can help focus an organization's data management initiatives -- but it isn't the same as data governance. Expert Anne Marie Smith explains why.Continue Reading

• customer data integration (CDI)

  Customer data integration (CDI) is the process of defining, consolidating and managing customer information across an organization's business units and systems to achieve a "single version of the truth" for customer data.Continue Reading

• 5 to-dos for your GDPR compliance checklist

  It's never too late to fine-tune your GDPR strategy. Expert Anne Marie Smith suggests a current state analysis of your PII protections, drafting a data privacy policy and more.Continue Reading

• Cloud vs. legacy ERP systems: Tug of war intensifies for SMBs

  Aging legacy ERP systems at SMBs seem to be getting plenty of scrutiny these days. Heightened consumer demands, shifting technology landscapes and relentless market disruptions, not to mention maintenance costs, technical support and obsolescence, ...Continue Reading

• Apache Hive

  Apache Hive is an open source data warehouse system for querying and analyzing large data sets that are principally stored in Hadoop files.Continue Reading

• Good data quality for machine learning is an analytics must

  As companies add machine learning applications, they need to really understand -- and be able to improve -- their data. That's where data quality initiatives come in.Continue Reading

• The benefits of columnar storage and the Parquet file format

  What's behind Apache Parquet's growing popularity? It may be the file format's columnar storage orientation, which leads to benefits including improved query performance.Continue Reading

• Four first steps for customer data management

  Forrester's Mike Gualtieri details how to develop a unified plan to manage customer data that gives business users what they need to manage CRM programs.Continue Reading

• Three factors for protecting sensitive data in the GDPR era

  Data privacy is a hot topic nowadays thanks to GDPR and the Facebook data scandal. But how do data security, access control and data protection differ?Continue Reading

• What's the difference between DDL and DML?

  What's the difference between DDL and DML? Get the answer and see examples of data manipulation language and data definition language commands for SQL databases.Continue Reading

• What goes into a customer analytics data integration framework

  Customer data integration is a minefield for IT teams to navigate. But incorporating a set of core technical functions into an integration architecture can ease the process.Continue Reading

• Google Cloud data lake fuels cloud payment processing flow

  To create a cloud payment processing system, Global Payments first had to deploy a data lake in the Google Cloud. Getting quick user feedback was another early step.Continue Reading

• Develop smart AI in CRM strategies to win and keep customers

  Of the three words that comprise customer relationship management, one word binds the other two. As necessity and competition dictate that CRM upgrade itself with artificial intelligence and flights to the cloud, what counts most in ...Continue Reading

• GDPR compliance requirements drive new winds of data privacy

  Hello, GDPR. May 25 is the witching hour for enforcement of the EU's much-discussed GDPR compliance requirements -- and it's a harbinger of more changes to come.Continue Reading

• What does the GDPR definition of personal data include?

  The definition of personal data in the EU's GDPR data protection rules is broad enough to include any type of data that can be used to directly or indirectly identify a person.Continue Reading

• Data expert: GDPR deadline is an opportunity, not a burden

  There is stress as the EU's General Data Protection Regulation compliance deadline nears, but the GDPR privacy movement is a good thing for data policies, advises consultant Daragh O Brien.Continue Reading

• TensorFlow

  TensorFlow is an open source framework developed by Google researchers to run machine learning, deep learning and other statistical and predictive analytics workloads.Continue Reading

• Hyperledger Fabric offers path to enterprise blockchain future

  Blockchain arose from bitcoin, but it's looking to find a place in the enterprise. Frameworks like Hyperledger Fabric could smooth out the technology's rough side for business uses.Continue Reading

• Data lake concept needs firm hand to pay big data dividends

  Data lakes pose technology deployment and data management challenges that can leave analytics users high and dry if the implementation process isn't handled properly.Continue Reading

• Slow to gain traction, AI apps on the verge of explosion

  From chatbots ("Can I help you?") to killer bots ("I'll be back."), artificial intelligence runs the gamut of applications and emotions like no other technology. It's been nearly 70 years since AI first came into consciousness with humankind, yet ...Continue Reading

• Three ways to turn old files into Hadoop data sets in a data lake

  Hadoop data lakes offer a new home for legacy data that still has analytical value. But there are different ways to convert the data for use in Hadoop depending on your analytics needs.Continue Reading

• Hadoop data lake

  A Hadoop data lake is a data management platform comprising one or more Hadoop clusters.Continue Reading

• How AI and IoT will influence data management in 2018

  AI and IoT will alter the data management landscape in 2018, according to analyst James Kobielus. AI will need regular updates, and DevOps will become more prevalent as a result.Continue Reading

• MariaDB

  MariaDB is an open source relational database management system (DBMS) that is a compatible drop-in replacement for the widely used MySQL database technology.Continue Reading

• dark data

  Dark data is digital information that is not being used. Consulting and market research company Gartner Inc. describes dark data as "information assets that an organization collects, processes and stores in the course of its regular business ...Continue Reading

• Database architecture design has to guard against DBMS chaos

  The proliferation of database technologies gives organizations more options to meet data processing needs. However, a strong architecture strategy is a must to avoid a DBMS free-for-all.Continue Reading

• Business benefits of DevOps empower citizen developers

  The mysteries once associated with coding and application development are gradually giving way to the forces of market demand for speed and simplicity. No sooner did we get somewhat comfortable with DevOps and what it means that a new wrinkle...Continue Reading

• Good data quality for analytics becomes an IT imperative

  High-quality data is a must for analytics applications. That's driving more demand for data quality tools, but quality initiatives are still maturing in many companies.Continue Reading

• Cloud big data clusters test users on migration, management

  There are good reasons to move big data systems to the cloud, but doing so also poses challenges for IT teams on migrating workloads and then managing clusters and system instances.Continue Reading

• Database protection methods expand to shield data from attackers

  Database vendors have beefed up the security tools in their software -- and that's a good thing because attackers are increasingly targeting database systems to steal sensitive data.Continue Reading

• Google Cloud Spanner

  Google Cloud Spanner is a distributed relational database service that runs on Google Cloud.Continue Reading