Monthly Archives: April 2018

Best Database Certifications In The Year 2018

There are lots of database platforms that come and go, in the past 30 years but there is no question about database technology to be an important component of various kinds of applications and computing tasks.

There is no much attraction for database certifications similar to cloud computing, storage or computer forensics. But the actual fact is that there is always a requirement for experienced database professionals at various stages with respect to many related job roles.

For getting a good grasp of the present database certifications it is useful to be around the ground for particular database-related job roles. As you get to read about lots of database certification programs, just keep these things in mind:

  • Database Administrator(DBA): It is meant for configuring, installing and maintaining a database management system (DBMS). It is often linked with a particular platform like Oracle, MySQL, SQL Server and others.
  • Database Developer: Working along with proprietary APIS and works with generic building applications that contact the DBMSs.
  • Database Designer/Database Architect: Data requirements are researched for particular applications or users and designs database structures and application functions match.
  • Data Scientist or Data Analyst: Data is analyzed with the help of this from various disparate sources for identifying the past hidden insight, find the meaning behind the data and make business-particular recommendations.
  • Data Mining or Business Intelligence (BI) Specialist: It assists in analyzing, dissecting, reporting of significant data streams like customer data, supply chain data, transaction data, and histories.
  • Data Warehousing Specialist: The data can be assembled and analyzed which specializes from various operational systems to set up the data history, produce reports and forecasts and assistance of general ad-hoc queries.

These database job roles are offered with proper attention with two significant types fo technical issues for the future database professionals. At first, a good general background in relational database management systems having a proper knowledge of SQL is the basic prerequisite for various database professionals.

There are lots of database technology standardization techniques and among them, there is whiz-bang capability of the databases and database applications for providing from vendor-specific technologies, proprietaries. There are lots of knowledge and heavy-duty database skills that are linked to particular platforms like lots of Oracle products, Microsoft SQL Server, IBM DB2 and much more.

Not Only SQL is the other name for NoSQL database and at times non-relational can maintain lots of data types like semi-structured, structured, unstructured and polymorphic. In big data applications, NoSQL database is mostly used which is mostly linked with certifications for data scientists and business intelligence.

If you check out for the featured certifications in detail then you would consider their fame with employers. There is a continuous change on a daily basis but such numbers offer perspective on database certification demand.

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Reference site: Tomsitpro

Author name: Ed Tittel and Kim Lindros

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FoundationDB in detail

A distributed scalable transaction (ACID) key-value database with crazy thorough testing is called as FoundationDB. It was quite good that three years ago the complete company was bought by the Apple and Shut value. Irrespective of that unfortunate fact, there are lots of companies which uses FDB even now:

  • Snowflake: It is a known fact that FoundationDB is a significant part of our architecture and it has permitted for building few truly amazing and differentiating features.
  • Wavefront: As per VMWare, the cloud monitoring and analytics has been done with the help of FoundationDB extensively for about 50 clusters spanning petabytes of data in production.
  • Update: For the purpose of FoundationDB, online warehouse management system as a distributed event store holding various events and coordination for nodes in the cluster is used.
  • Data Model

There is weird design for FDB which has a key-value database. It is possible to think of it as a giant sorted dictionary and here both keys and values are byte arrays. With that dictionary, it is possible to do the normal operations while linking with lots of operations inside a single ACID transaction.

You can find very low-level interface as it permits you to construct your own data layers on top after hosting them on a single cluster:

  • Tables
  • Object Storage
  • Lists
  • Graphs
  • Indexes
  • Blob Storage
  • Distributed commit-logs
  • High-contention queues
  • pub/sub

However, FoundationDB is regarded as a database constructor.

  • Testing

The database is developed inside by the FoundationDB called deterministic simulation. There are some IO operations like disk and network that was abstracted which was permitted by injecting various faults while running clusters inside the load inside an accelerated time.

Let us view some examples of faults unveiled in such environments:

  1. Buggy Router
  2. Network Outage
  3. Disk Outages
  4. Machine Reboots and Rreezes
  5. Human Errors

With the help of a single thread, a complete cluster can be stimulated and this is what a deterministic stimulation is regarded as.

When there is a self-manifestation of bug on its own it is possible to replay that simulation until you want it to happen. It will be the same always and just you need to keep the initial rand-seed.

Inside a custom scheduler, the simulated system is run by enabling you to force the time to move ahead similar to any discrete-event simulation. If you are aware that for the next 10ms there is nothing interesting that is going to work it out and it is instantly fast-forwarding the world to that point in time.

  • Simplified simulation of TCP/IP: Reorder buffers, SEQ/ACK numbers, connection handshakes are a part of this. You can also find a proper shutdown sequence and no packet re-transmissions.
  • Durable node storage: As per machine folders there is a use of LMDB database.
  • Simulation plans assists in specifying how we want to run the topology which is simulated and this includes a graceful chaos monkey.
  • Simulating power outages: by removing the future of the affected systems.

Network profiles: latency configuration ability with packet loss ratio and logging with respect to the network connection.

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Reference site: abdullin

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Difference Between Apache Spark And Apache Nifi

Although there are new technologies pouring in on a daily basis it is not sure to know the right applications. Here are two technologies we are about to discuss Apache Spark and Apache Nifi. A cluster computing open source framework is none other than Apache Spark and its motto is to offer an interface for programming entire set of clusters with fault tolerance and data parallelism.

There is another software project called Apache Nifi and its aim is to automate the flow of data among software systems. The flow-based programming model is the basis for its design which has operations with the cluster’s ability. It is quite easy to rely on and use a powerful system for distributing data and process.

The differences between Apache Nifi and Apache Spark are mentioned below:

  1. A data ingestion tool called Apache Nifi is used for delivering a simple to use, reliable and powerful system so that distribution and processing of data among resources becomes easier and moreover ApacheSpark is quite a fast cluster computing technology which is created for rapid computation by quickly making the use of queries which are interactive in-stream processing capabilities and memory management.
  2. In a standalone mode and a cluster mode, Apache Nifi works whereas Apache Spark works well in the standalone mode, Yarn and other kinds of big data cluster modes. Guaranteed delivery of data is present in the features of Apache Nifi with proper data buffering, prioritized queuing, Data Provenance, Visual Command and Control, Security, Parallel streaming capabilities along with features of apache spark with fast speed processing capabilities.
  3. A better readability and a complete understanding of the system offers visualization capabilities and the features are dragged and dropped by Apache Nifi. It is possible to easily govern and manage the conventional processes and techniques and in case of Apache Spark, these kinds of visualizations are viewed in a management system cluster like Ambari.
  4. The Apache Nifi is linked with the restriction to its benefit. A restriction is offered by the drag and drop feature of not being scalable and offers robustness when combining with various components and tools with Apache Spark along with the commodity hardware which is extensive and becomes a difficult task at times.
  5. There is other reported limitation along with the streaming capabilities linked with Discretized Stream and batch or Windowed Stream and the data sets offer a lead for instability at times.

Use Cases of both include;

  • Apache Nifi: Data flow management along with visual control data size Arbitrary data routing among disparate systems
  • Apache Spark: Streaming Data Machine, learning Interactive Analysis, Fog Computing

Final Words;

You can finish the post by saying that Apache Spark is quite a tough war horse and moreover Apache Nifi is a weak horse. Each of them has their own advantages and disadvantages to being used in their respective areas. Just consider the right tool for your business.

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Reference site: educba

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Apache Beam

If you don’t like using multiple technologies to achieve lots of big data tasks then you need to consider Apache beam with a new distributed processing tool from Google that is currently developing at the ASF. Due to some difficulties of the big data development, there is a requirement for using various different technologies, frameworks, languages, APIs, and software development kits. An abundance of riches for big data developers has been offered by the open source movement and it has enhanced pressure on the developer to choose the perfect tool for the things she is wanting to accomplish.

This is quite difficult for those with a new development in big data application which could reduce or hinder the adoption of open source tools.

To remove some of the second-guessing the web giant is wanting to remove some painful tool-jumping along with Apache Beam which is placing a single programming and runtime model by not unifying development for interactive batch and streaming workflows but it also offers a single model for both on-premise and cloud development.

Depending on the technology used by Google it uses the Cloud Dataflow service which the company unveiled in 2014 for the current generation shared data processing challenges.

In the combination of the Dataflow Software Development Kit (SDK) the open source Apache Beam project along with the runner series extend out to run-time frameworks, Apache Flink, and Cloud Dataflow itself which can be freely tried by Google for charging you money in the usage of production.

A unified model is offered by Apache Beam for both designing and executing lots of data-oriented workflows within a data processing, data integration, and data ingestion as per the Apache Beam project page. Earlier the project was termed as Apache Dataflow before seeking the Apache beam moniker actually works on lots of Apache Software Foundation projects. The Beam runner for Flink is developed and maintained by the data Artisans and is joined by Google in the project.

Just consider you have a MapReduce job and now you need to combine these jobs with Spark which needs lots of works and cost. After this, the effort and cost you need to change to a new platform have to refactor your jobs again.

An abstraction layer is offered by data flow between the execution runtime and code. A unified programming model is permitted by the SKD for implementing your data processing logic with the help of Dataflow SDK that runs on various different backends.There is no need to refactor or change the code anymore.

In the Apache Beam SDK, there are four major constructs as per the Apache Beam proposal and they are:

  • Pipelines: There are few computations like input, output, and processing are the few data processing jobs actually made.
  • Pcollections: For representing the input there are some bounded datasets with intermediate and output data in pipelines.

For lots of batch processing or streaming goals, beams can be used similar to ETL, stream analysis and aggregate computation. For lots of batch processing goals or streaming is used by Beam like stream analysis and aggregate the computation.

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Reference site: datanami

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Apache Falcon

Enterprise challenges are addressed by Apache Falcon which is linked to Hadoop data replication, lineage tracing, business continuity by deploying a framework of processing and data management. The data life cycle is managed by falcon centrally for facilitating the data in a quicker way for business disaster and continuity recovery which offers the foundation compliance and audit by tracking the collection lineage of audit logs.

  • Define Falcon

An enterprise is permitted by Falcon for processing a single massive dataset which is present in HDFS in various ways- for batch, streaming, and interactive applications. With the increase in the value of Hadoop data the significance of cleaning the data preparation for enterprise intelligence tools which removes the cluster when it outlives the useful life.

The development and management of the Falcon simplify the data processing with a higher layer of abstraction by seeking the complex coding outside data processing by offering the out of the box data management services.

There are other HDP components that are leveraged by Falcon framework like Oozie, Pig, HDFS by enabling the simplified management by offering a deploy, define and manage data pipelines framework.

  • Working of Falcon

As a standalone server, Falcon runs and as a part of Hadoop cluster.

Entity specifications are created by a user and submit to Falcon by the Command Line Interface (CLI) or REST API. The entity specifications are transformed by Falcon into repeated actions via a Hadoop workflow scheduler. With the schedule, all the functions and workflow state management needs are delegated.

Here are the following entities that define the part of the Falcon framework:

  1. Cluster: The interfaces are represented by a Hadoop cluster
  2. Feed: A dataset is defined similarly to Hive tables or HDFS files with replication, location schedule, and retention policy.
  3. Process: Processes and Consumes Feeds
  4. Falcon’s purpose: Replication

Data replication is involved with various enterprise conversations at some point and these ranges from the simple “I require multiple copies of data” to the excessive complex “ I require certain subsets of staged, presented and intermediate data replicated among clusters in a failover scenario and that requires each dataset for having a various retention period.

These problems are typically solved by custom-built applications, which can be very much time consuming, with a good challenge for maintenance and is error-prone. The custom code is avoided by Falcon and rather express the processing pipeline and replication policies within a simple declarative language.

In the scenario given below the staged data travels via a sequence of processing which is taken by business intelligence applications. A replica of this data is needed by the customer in a secondary cluster. When compared to primary cluster, the secondary cluster is smaller with lots of subset of data to be replicated.

The datasets are fined by Falcon and process the workflow at designated points. The data of the secondary cluster is replicated by Falcon. The processing of Falcon is orchestrated and scheduled by the replication events. The final result is in case the failover critical staged and presented stored in the secondary cluster.

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Reference site: hotonworks

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Apache Nifi Overview

Currently, at multiple end-point systems, you can find data gathering and here are a few click streams to name like legacy systems, sensors, weblogs, clickstream. There is a new challenge in the process of gathering data and maintaining them. Data flows are scheduled relying on a set of input conditions, for instance, Apache Falcon permits you to configure and schedule data.

For gathering data from various end systems and aggregating them, the flume is a very good fit. As a combination address while this system independently talks a lot about the key requirements for gathering data at a real-time shows few significant aspects of data flow and data collection.

There are different delays in data coming from the similar systems and sensor which are not the same. This is termed as gathering data at the jagged edge or ragged edge. Moreover, such systems are at various geographic locations with latency and network bandwidth.

For about 8 years if the platform Nifi was around before it was unveiled by open sourced and incubated ASF.

  • Guaranteed Delivery

Even during very high scale, a core philosophy of NiFi has been having a guaranteed delivery. With the effective use of purpose-built persistence, this can be achieved by write-ahead log and content repository. For very high transaction rates, they are designed to permit it when they are combined together. At least once semantics of data delivery is permitted by Nifi.

  • Data Buffering/Back Pressure and Pressure Release

All the queued data that is buffered is assisted by NiFi along with the ability to offer back pressure as those queues touch limits which are specified for aging off data as it reaches a specified age.

  • Prioritized Queuing

The setting of more than one prioritized schemes is permitted by NiFi for how to retrieve the data from a queue. There is the oldest default key first but there are times when the data must be newly pulled with largest first or few another custom scheme.

  • Flow Specific QoS

In the place where data is completely important and does not tolerate the loss. You can find few times about its process and its delivery within seconds of any value.

The fine-grained is enabled by NiFi by flowing particular configurations of these concerns.

  • Data Provenance

There are indexes, records that are made automatically by NiFi in making the existing provenance data as objects flow via the system. In assisting the compliance this information becomes quite important which is also troubleshooting with optimization and other scenarios.

  • Visual Command and Control

A visual establishment is enabled by NiFi with data flows in real-time. It offers a UI based approach for building design flows. Apart from that, it permits you to include or delete data flows in a deployed flow.

  • Flow Templates

There is a high pattern orientation followed in data flows and there are lots of various ways to solve a problem and it assists greatly in sharing those best practices. Subject matters are permitted by templates for building and publishing their flow designs for various benefit and collaboration on them.

  • Clustering

For scaling out the cluster usage, NiFI is designed with lots of nodes combined as mentioned above. The most effective thing is the usage of NiFi’s site to site feature as it permits the NiFi and a client for communicating with each other for exchanging data on particular authorized ports.

  • Security

A data flow with system to system is not only good till it is secured and NiFi at all points in a data flow provides secure exchange via the use of protocols with encryption like 2-way SSL. Apart from that NiFi permits the encryption and decryption content flow with the shared-keys utilizations or other mechanisms on a various side of the recipient or sender equation.

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Reference site: thedatateam

Author name: Kaushik Chatterjee

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Apache Flink VS Apache Spark

In the Big Data landscape, Spark and Apache Flink are major technologies. There is a confusion about each of their work and purpose. Let us see them in detail:

  • Streaming Data

This implies that data there is an ongoing flow of data. In batch systems, processing streaming data offers technical complications that are not inherent. The technological advancement over here is the ability offered to do that by making streaming products similar to Spark, Flink, Kafka, and Storm which is quite important. The decisions can be made easily as it permits the organizations based on what is happening currently.

  • What is the purpose of Flink and Spark?

A replacement for the batch-oriented Hadoop System is none other than Apache Spark. It has a component termed as Apache Spark Streaming. Streaming can be done only with the help of Apache Flink. In-memory databases are done by both Flink and Spark that do not force their data to store. You cannot analyze the current data as there is no reason to write it for storage. There are other real-time systems like Spark/Flink that are very much sophisticated similar to a Syslog connection which is built over a TCP built inside every Linux system.

  • What is the difference between Apache Spark and Apache Flink and specifically Apache Spark streaming?

Ideally, Spark and Flink would make it same. The prime difference is that Flink was constructed from the ground up as a streaming product. Streaming has been included by Spark into their product.

Let us understand the technicalities of both

  • Spark Micro Batches

There are discrete chunks of data that is divided by Spark called micro batches. Then it again starts with a continuous loop. A checkpoint is done on streaming data for breaking the finite sets in Flink. At this checkpoint, the data which is coming is not lost when considering this checkpoint it is preserved for future in both the products. In most of the cases, you will find some lag time processing live data so ideally splitting it into sets does not matter.

  • Flink Usage for batch operations

Similar to Hadoop, a spark was built for running over static data sets. The streaming source is stopped when Flink comes into the picture. The data is processed by Flink in the same way irrespective of it being finite or infinite. Dstreams is used by Spark for streaming data and RDD for batch data.

  • The Flink Streaming Model

For projecting that a program processes streaming data implies that it unveils a file and never closes it. This is similar to keeping a TCP socket open and Syslog works for this purpose. On the contrary, batch programs, open a file, process it and then close it. It has created a way to checkpoint streams as per Flink without having to open or close them. After that simply try to run a type of iteration that assists their machine learning algorithms to keep on the run in a faster way when compared to Spark.

  • Flink versus Spark in Memory Management

When compared to memory management, flink has a different approach. When memory is full, Flink pages out to disk and this happens with Linux and Windows. When Spark runs out of memory, Spark crashes. But there are no means of losing the data as it is fault tolerant.

  • Spark for Other Streaming Products

Kafka is used for working on both Flink and Spark and the streaming product is written on LinkedIn. Flink combines its work with Storm topologies.

  • Cluster Operations

On a cluster, Spark or Flink can run locally. For running Flink or Hadoop on a cluster one normally uses Yarn. With the help of Mesos, Spark is usually run. You need to download the version of Spark if you want to use Spark with Yarn that has been compiled with Yarn support.

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Reference site: Developintelligence

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Apache Flink

During the days of complex event processing, we have been explaining the future of streaming that was bright. It was also said that the same data explosion created the emergency for Big Data that is producing demand for making the data actionable instantly.

Previous month there was another hit that came as we looked at the DataTorrent which is the company that works on the Apache Apex project which has applied a visual IDE for making streaming accessible to application developers.

There are various open source streaming engines like Storm, Apex, Heron and among these Flink does lots of streaming. It is mostly like the reverse image of Apache Spark in both put batch and real-time on the same engine doing away with the required Lambda architecture. For querying data in tables both have their own APIs and both of them have libraries or APIs for real-time processing and batch along with machine learning and graph.

In what way does Flink depart from Spark? Flink is mostly used for streaming and is expanded for batch while Spark was mainly for batching and its streaming version: micro-batching.

For some amount of time, the same metaphor could be made for Storm but it gets rid of the libraries and assist with critical features like scaling limitations.

It is quite wanting to state that such comparisons come with big data computer which has left the station. It is regarded as one of the top five Apache Big Data projects as per the hackers of the Flink and commits activity for being the first one.

Commercial vendor support is nurtured by Spark and mostly it is supported by all Hadoop distributions for doing all major cloud providers.

With data preparation tools and a roster of analytics is grown the data preparation is baking under Spark of the hood.

Why are we seeing this conversation? The first initiator is the Spark and the link folks are not trying to be advantageous for the people. They are not focusing on interactive analytics or constructing the complex machine learning models.

A capability which is mostly reversed for databases is focused b Flink for handling the stateful applications. Flink’s application re-implemented microservices for managing the state. The I/O of the database are avoided for real-time applications which decrease the latency and overhead.

This is not regarded as a new idea for n-tier applications for managing the Java middleware layer. Transaction motors are abstracted from the database.

Where fast databases are constructed on in-memory or all-Flash storage has to lead to the practical approach and there are lots of known real-time use cases where moving state maintenance out of database will make a difference.

Managing IoT networks, keeping current with e-commerce clickstreams are few sampling with travel reservations and connected cars. This does not imply that Flink will replace databases as the data may eventually get persisted and the main component of processing at times is performed before the data gets the database.

There are works going on for commercial support for Flink and it has drawn the initial stages of grassroots of about 12000 people all over the world. Data Artisans and MapR have co-authored an excellent detailed study of Apache Flink that is free for download.

In Google’s Cloud Dataflow service, the Apache Beam project was applied and is rescheduled by offering a data in motion processing where you need to exchange in and out the actual compute engines of various choices.

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Reference site: Zdnet

Author name: Tony Baer

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Azure Service Fabric

  • Introducing Azure Service Fabric

The basis of Azure is hidden away with service fabric which is tough to explain. But we explain it all the time in the tools for constructing self cloud-native software. Azure Event Hub’s center is the basis for the IoT platform and its CosmosDB databases and SQL have various consumer services and enterprise for using it on a daily basis. The same tools are accessed with the help of Azure Service Fabric that is used by Microsoft for running and managing its self-services for constructing them into your self-code.

Your application lifecycle is handled by Azure Service Fabric with APIs for offering them extra platform access ahead of pure code which is standing alone. It also assists its own message/actor microservices along with hosting ASP.Net Core code.

Similar to processes, services can run on a native basis or it can be hosted by them in containers which offer the option in a speedy way that gets the existing code to Azure’s PaaS.

  • Get Initiated With Azure Service Fabric

Maybe the fastest way to initiate the development of Service Fabric is Reliable Service Framework. This is a set of APIs that combine with Azure Service Fabric’s application lifecycle management features. In any language that is assisted, you can write the code with your own application framework choice. There are either stateless or stateful Services and for handling external storage to handle state, stateless services are used.

It is quite interesting for having a stateful option as it uses Service Fabric’s self-tools of handling application state. It is quite interesting to have a stateful option as it uses Service Fabric’s own tools for managing application state. There is no requirement of considering high availability or scaling that is handled for you.

  • Scalable Concurrency With Actors

A benefit of the Reliable Actor framework must be taken with born-in-the-clouds applications. For implementing virtual actors, reliable services are extended. With the help of actor/message pattern for handling microservices works well as its basic concurrent systems model scales in a fast way for handling various actors working on a similar basis.

For every scenario, a reliable actor is not enough for working the best when you can split your code down into few blocks of computation with single-threaded objects either withholding their self-state or nation-state.

There are many complex distributed computing problems with the requirement of carefully thinking about the mapping objects and using them in your applications.

  • Azure Service Fabric Goes To An Open Source

Its open-sourcing service Fabric is announced by the Microsoft on a recent basis by shifting the development model by accepting the third party pull requests along with public, open design process.

It is a known fact that the first tranche of open source code is Linux-based and the Microsoft development has marked the Windows-based code that runs on Azure for a good follow up. On a GitHub, there will be a good development which lots of initial work targetted on finishing the transition from Microsoft’s inner problems to a public process that is faced.

An open source Azure Service Fabric is delivered by the Microsoft as per its plan for initiating the Linux branch of the code. It is a recent code and uses a good tool when compared to the Windows version by making it simpler with the branch for shaping the public release.

The Windows tools are quite complex for about ten years or so of history that requires being unraveled and refactored.

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Reference Site: Infoworld

Authorname: Simon Bisson

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