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In Tableau, strings or string data, are data made of text. String functions exist to help you manipulate your string data. There about twenty string functions in Tableau. In this article, I am going to introduce you to ten (10) important string functions in Tableau, and how to use them. You might be wondering “why are string functions so important?” Let’s assume a scenario where you want to pull the first name of all your customers into a new field; a string function will handle that task for you. String Functions In Tableau Important String Functions in Tableau Please note that all the string functions in Tableau are of extreme importance. This article is just exploring 10 of them, and they include: ASCII CONTAINS FIND LEFT LEN MID REGEXP_MATCH REGEXP_REPLACE SPLIT TRIM 1. ASCII This returns the ASCII code value of the first character in a string ASCII ( String ) ASCII (“authors”) = 97 2. CONTAINS This returns a TRUE if the specified substring is present in the given string. CONTAINS ( String, Substring ) CONTAINS (“Calculation” , “alcu”) 3. FIND This returns the position of the specified substring within the given string and returns 0 if the substring is not found. FIND (String, Substring, [ Start ]) The first character in the string is position 1 (not a 0 index). FIND (“Calculation”, “alcu”) = 2 If the start argument is defined, any instance of the substring that appears before the start position are ignored FIND (“Calculation”, “a”, 3 ) = 7 4. LEFT LEFT ( String, num_chars) This returns the specified number of characters from the start of the given string. LEFT (“Calculation”, 5 ) = “Calcu” 5. LEN LEN (String) This returns the number of characters in the given string. LEN (“Calculation”) = 11 6. MID MID (String, Start, [length]) This returns the characters from the middle of a text string given a starting position and a length. The first character in the string is in position 1. If the length is not included, all characters to the end of the strings are not returned. MID (“Tableau Software”,  9 ) = “Software” If the length is included, up to that many characters are returned. MID (“Tableau Software”, 2, 4) = “able” 7. REGEXP_MATCH REGEXP_MATCH ( String , Pattern ) This returns true if a substring of the provided string matches the regular expression pattern. REGEXP_MATCH ( ‘ – ( [1234] . [ The.Market] ) -’ ,  ‘\ [\s*(w*\.)  (  \w*\s*\]) ‘ ) = true 8. REGEXP_REPLACE REGEXP_REPLACE ( String, Pattern, replacement) This returns a copy of a given string where the matching pattern is substituted with the replacement string. REGEXP_REPLACE ( ‘abc123’, ‘\s’, ‘-’ ) = ‘abc–123’ 9. SPLIT SPLIT ( String , delimiter, token number) This returns a substring from a string as determined by a delimiter (a separator) extracting the characters from the beginning or end of the string. SPLIT (‘a/b/c/d’ , ‘ / ‘ , 2 ) = ‘b’ A negative token number can also be used. SPLIT (‘a/b/c/d’ , ‘ / ‘ ,  -2 ) =  ‘c’ 10. TRIM TRIM (String) This returns the string with both leading (LTRIM is used for this only ) and trailing (RTRIM is used for this only ) whitespaces removed. TRIM (“  Budget   ”)  =  “Budget” To learn more about all the string functions in tableau, click here.

Salesforce is a CRM which stands for Customer Relationship Management.  In very simple terms, it is an online tool that helps businesses manage their customer information (basically a database of customers) efficiently. Salesforce is a software for every organization, and in this article, I’m going to tell you why. Salesforce organizes data into objects and records just like in an excel spreadsheet where objects are like tabs and a record is like a single row of data. It can also be a unified place to do stuff with the company’s information stored on the secured cloud which makes data accessible anywhere and anytime in the world. Salesforce: A must-have Here are some of the reasons why the software is a must-have for every organization: Flexibility in capacity One of the most significant unique selling points of the Salesforce platform is its high degree of adaptability. The objects to be found in Salesforce can be set entirely in line with your desires at any time. As a user, you are not tied into certain set page layouts, workflows, and processes, and this makes Salesforce’s ecosystem more flexible than other similar systems on the market. Security Salesforce has some security basics that keep your data secured by authenticating users, giving only users access to the data in the company, also sharing objects and fields while monitoring the organization’s security. Countless options with various apps In addition to the clouds we have designed ourselves, Salesforce’s ecosystem also comprises applications you can purchase through the App Exchange. A major benefit compared to other suppliers, where you must settle for integrated tooling from the same supplier. The AppExchange features all kinds of apps capable of supporting your processes (recruitment, sales, marketing, finance, etc.). It is often the case that these apps have been developed by experts in the relevant fields. Consider Data loader (data import), Mailchimp (e-mail marketing), Grow promoter (NPS research) and Ebsta (integration with Gmail) for example.  Lead Management Salesforce.com can help your company track your leads from clicks, that is from being a prospect to closing the deal, while continually optimizing your campaigns across every channel. Make smarter decisions about where to invest your marketing money, thereby reducing financial loss in your company. Reports Our CRM analytics software keeps you updated with customized sales forecasting reports that you can build with ease. Just drag and drop the fields, filters, groupings, and charts that you want, and get an immediate real-time view. Salesforce can generate 1000+ reports in less than a second with just a few clicks on your data which can save your company from quite several stress and time wastage. Contact Management You can have a complete view of your customers, including activity history, key contacts, customer communications, and internal account discussions. Gain insights from popular social media sites such as Facebook, Twitter, LinkedIn, and YouTube all right within Salesforce. Get an accurate view of your entire business with comprehensive forecasts. See a complete view of your entire pipeline and your business, and act where necessary. Provide rapid updates to help management make decisions. Then easily apply your judgment to forecasted amounts at the rep, period, and summary levels. You can also view details about any previous adjustments that you or your team provided while leaving the underlying opportunity data intact.

Big Data Analytics tools are generally used to provide substantial analysis of a large set of data. It helps to find market insights, trends, customers’ preferences, and diverse information. These tools vary in features, efficiency, and complexity. In this article, I have compiled the top 7 data analytics tools this year. Here are the top 7 Big Data analytics tools in 2020. 1.  Tableau Tableau is a well- known software in the field of data analysis and it’s a good choice for folks with no knowledge of data science, working in businesses across organizations. Although may seem similar to an excel spreadsheet, it has more features and advantages over excel. In Tableau, no more chunk of boring data with its VizQL data visualization technology. Tableau users enjoy the benefit to reuse existing skills in the Big Data(context) settings. It uses standardized SQL to query and interface with Big Data systems thereby making it easy for enterprises to use an existing database to get insights. Tableau allows quick data lookup and analysis by incorporating “Hyper” which is its memory data engine.  You can read more about Tableau here. 2. Zoho Analytics  One of the notable features of Zoho Analytics is its ease of use. You do not need the help of an IT or data scientist expert to gather information from data. This software has both a spreadsheets style interface and an easy drag and drop interface. Zoho is also by far, one of the top data analytics tools in 2020 In a world of various data sources, Zoho Analytics enables access to a wide array of data storage. Files in your local storage, several important business applications, cloud drives, databases, and custom-built applications are all included. If you are searching for an analytical tool that will give you easy and accessible data insight to every employee/worker in your business, then Zoho is a good fit for you. 3.Microsoft Power BI  A characteristic feature of Microsoft power BI is its ease of use. It has been a choice for analyst companies in the business intelligence field. Over the years, the major differentiator of Power BI is its integration with the Azure Data Lake storage for analysis of advance big data.  It is one of the top big data analytics tools for a while now. 4. Cloudera  A distinguishing feature of Cloudera is its close ties with the core Hadoop Big Data. It has an intensive understanding and key expertise in Hadoop. Cloudera is a good option for businesses who want to create and process predictive analysis models with several integrated tools.  It is one of the top data analytics tools this year. 5. SAS Visual Analytics  SAS Institute has been in the analytic marketplace for a long while. It is recognized for deep competence in data analysis. It is suitable for business intelligence and data reports. 6. Oracle Analytics Cloud   Oracle Analytics Cloud is also a big data analytics tool. It is known for its self-service in Big Data Analysis on a consumption usage model. Businesses who are familiar with Oracle tools will likely be the ones most interested in Analytics Cloud offering. Oracle Analytics prides itself on its ability to bring together multiple data sources. 7.Hitachi Vantara Pentaho  Although Hitachi would most likely not be linked with Big Data, its open-source nature is a major differentiator and strength. Hitachi is a good option for a business that has several types of data and big data sources. Pentaho users enjoy the ability to ingest and blend data quickly from different sources.

SQL is something you probably have come across in several articles or on social media. The first time I heard about SQL was from a friend’s retweet on Twitter. Today, I want to share with you an educative article on SQL, and why SQL is an important skill for you to learn. Here are some of the things I am going to be covering in this article What is SQL? Difference between a Programming language and query language Who can learn SQL? What is MYSQL? Jobs that require SQL How long will it take to learn SQL? The three-letter abbreviation SQL, pronounced “ess-que-el” or “see-kwell” is quite a hot skill to learn. SQL means Structured Query Language. Yep, you got that right. It is not a programming language, at least not exactly. It is a “query language”. What does a query language mean? Simply put, a query language is a computer programming language that retrieves information from a database. So, SQL is a query language that is used to communicate with the database. It is used to update or retrieve data from a database. If you are a beginner, you should consider learning SQL as it is much easier than programming language like Java, PHP, Java, C++. What is the difference between a Query Language and Programming Language? A programming language is a language used by humans to give instructions to a computer; the steps to take in solving a problem. A query language, on the other hand, is a language used to manipulate data. Who can learn SQL? Often, a lot of people say, “I can’t code, I don’t think I can learn SQL. I’m not a techie, how can I understand SQL? Luckily, anyone, anybody can learn SQL. You don’t have to know any programming language before you can learn SQL.  No technical skill is required. It’s a “come as you are” language. No prerequisite for learning. What is MYSQL? It is a relational database management system used to manage databases. It is an open-source software. This means it is free to use and essential for web developers because several applications and web are built on databases. What does MYSQL do? Here’s an illustration. A movie program such as Netflix, stores and transmit movies, documentaries, TV shows and anime on different devices. You can search easily for movies by using parameters like movie name, genre, actor, director, and others. Apps like that need a software to manage their SQL database. Here are some reasons why SQL is an important skill to learn. It is a highly demanded skill SQL is one of the languages used by Web developers, desktop developers, DevOps, and data analysts. Did you know that it’s not only used by tech companies?  According to Dice; one of the most popular job posting sites, listed SQL as the most sought-after skills by employers. Even more than Python or R. Isn’t that surprising? That shows how useful and relevant SQL is to companies. It is an important skill that everyone should learn, as long as they can. It is used widely  SQL is used everywhere, as long as there is a database account to manage. If you are considering a career in data science or analytics, then you should learn SQL. Several big tech companies such as Airbnb, Facebook, Amazon, Netflix, Twitter, Google, and lots more use SQL to query data and carry out an analysis. Are you still wondering why SQL is such an important skill to learn? SQL is easy to learn One thing I can assure you when learning SQL is that you will find it interesting. Why? Unlike programming languages, it is written in the English language so everyone can understand it. If you understand and can write basic English language, then you are good to go.  Luckily, several database engines can work well with every SQL code. You can work across all relational databases when you learn SQL. There are also several learning platforms to pick it up. High salary scale How much does a SQL developer take home?  According to Glassdoor, a SQL developer earns an average of $81,622 annually. That’s a good income. Right? Now that you’ve learned why you should learn SQL. What next? You can access several resources that teach SQL online including free and paid. Some courses at the university can also give you a profound knowledge of the language. You can also enroll in paid online training such as Cndro’s SQL class. It provides you with real-life projects and one-on-one training support. Click here to enroll now. Jobs that require SQL Data Analyst: Since data analysts work with data daily, SQL is a required skill for them.  It is easy to learn and understand. SQL helps data analysts gain access directly to large sets of data in the database without having to duplicate data into other applications. Data Scientist: Just like data analysts, data scientists also deal with data daily, even in larger volumes and gather it at a higher speed. Because SQL integrates well with programming languages like Python and R, you will be able to communicate your data easily and understandably to your company. Database Administrator: The database administrator ensures that data is stored and organized properly. They also generate different reports by querying the database and manage data replication. Back-end Developer: Back-end developers manage the internal workings of web applications. They are behind everything that happens before it gets to your browser. A typical setup for a backend is a web server, an application, and a database. Product Managers: They need to have an in-depth understanding of their products. How will they do that? By using data to take an audit of their product’s performance. Data doesn’t lie, they say. Mobile App Developers Android app developers have been using SQLite for over 20 years. They used it mainly for projects that need to be stored on a device. SQL powers this embedded database; SQLite. Marketers: If you are a marketer, you have to be data-driven. Why? Because you won’t always have the attention of the analysts or developers to explain the reports to you. You will be more productive and help your business if you can

Data encryption is basically the process of securing information in a way that it can only be accessed by a specific key. In this article, I am going to show you how to encrypt your s3 bucket using the s3 server-side encryption (SSE-S3). You can choose to create a new bucket, or encrypt an already created bucket. Another method of encrypting your data on AWS is through the Key Management Service. It is somewhat different from server-side encryption. You can find a step-by-step walk-through on it here When using server-side encryption, you are basically encrypting your data with a default manage key that will be generated by Amazon Web Services (AWS). In this post, I will walk you through server side encryption of your s3 bucket. I will also show you how to encrypt your data before uploading it to the bucket. Let’s get right to it Server-side Encryption of S3 bucket 1. Sign in to AWS Console (https://console.aws.amazon.com/console/home) 2. Drop down the “Services” tab and select “S3” in the “Storage” menu. An interface will be displayed to you where you can select the s3 bucket you want to encrypt; or create a new s3 bucket For the purpose of this article, I will be creating a new s3 bucket. 3. Click on the “Create bucket” button in the top right corner. 4. Input your bucket name and region. I have named our sample bucket as “blogtestbucket”. 5. After successfully creating a new bucket, select the “Bucket details” button in the top right corner. This will take you to your bucket page. There you will find the “Overview”, “Properties”, “Permissions”, “Management”, and “Access Points” tab. 6. Select the “Properties” tab and click on “Default encryption”. 7. Next, select the “AES-256” option. This is the option to use server-side encryption with S3-managed keys for your bucket. Click “Save” to successfully encrypt your s3 bucket using server-side encryption. Your “Default encryption” tab should look like the image below when you are done. Note: If you want to encrypt an already created bucket, skip steps 3 to 5. Proceed from step 6. Server-side Encryption of Data Just in case you want to encrypt your data using server-side encryption before uploading it to your s3 bucket, follow the following steps. It’s quite easy. 1. Select the s3 bucket you want to upload data into, and as expected, select the “Upload” button. 2. Select the file(s) you want to upload, and click “Next”. 3. Scroll down to the Encryption section and select the “Amazon s3 master-key” option. 4. Complete the uploading process and you are all set. If you found any aspect of this walk-through helpful, you can share with any of the buttons below. Questions are welcomed in the comments section.

WS Key Management Service (KMS) is an Amazon Web Services product that allows administrators to create, delete, and control keys that encrypt data stored in AWS databases and products. In this article, I am going to walk you through how to encrypt data using the AWS Key Management Service. We’ll be creating an encryption key, encrypting s3 bucket using KMS, and encrypting data using KMS. When encrypting your s3 bucket data in AWS, you can either use the AWS Server-Side encryption or go through the Key Management Service. To know how to carry out server-side encryption, click here. Creating a key The first step to encrypting your S3 bucket or your data using AWS KMS is to create your encryption key. Let me walk you through this process real quick. 1. Sign in to your AWS Console. (https://console.aws.amazon.com/console/home). 2. Drop down the “Services” tab. Select “Key Management Service”. It can be found in the “Security, Identity & Compliance” menu. Here’s what I mean: AWS Console >> Services >> Key Management Service 3. A new page will be displayed where you can select the “Create a key” button to create your encryption key. 4. Next up is to configure your key. Select the type of key that you want to create. You can choose to create a symmetric key type, i.e, a single encryption key. The other option is an asymmetric key type that contains a pair of a public and private key. For this article, we’ll be using a symmetric key type. 5. Finally, enter an alias and a description for your key. The description is totally optional Encrypting s3 bucket using KMS Now that you have created your key, you can proceed to encrypting your data with KMS. To encrypt an entire s3 bucket in AWS with KMS, follow these steps: 1. Drop down the “Services” tab in your AWS console and select “S3” in the “Storage” menu. 2. Select the S3 bucket you want to encrypt, or create a new one as the case may be. 3. This will take you to your bucket page. There you will find the “Overview”, “Properties”, “Permissions”, “Management”, and “Access Points” tab. Select the “Properties” tab and click on “Default encryption”. 4. Select the AWS-KMS option, select the key you created earlier and “Save”. This will encrypt your bucket with the Key Management Service. Your “Default encryption” tab should look like the image below when you are done: Encrypting data using KMS If you want to encrypt data using KMS before uploading to your s3 bucket, follow these steps: 1. Select the s3 bucket you want to upload data into, and as expected, select the “Upload” button. 2. Select the file(s) you want to upload, and click “Next”. 3. Scroll down to the Encryption section and select the “AWS KMS master-key” option, and select the encryption key. 4. Complete the uploading process and you are all set. If you found any aspect of this walk-through helpful, you can share it with any of the buttons below. Questions are welcomed in the comments section.

Tableau Server is a product from Tableau that allows you to use the functionality of Tableau without having to download and open workbooks in Tableau desktop.  It works like every other server that lets you store things safely and also provides a collaborative environment for working. Tableau users publish data sources to the server when they want to share data connections they’ve defined. It is important to know how to get Tableau Server user details and data sources from tons of data sources. Tableau Server Client(TSC) is a Python library for the Tableau Server REST API. You can read about TSC here. There are instances where a lot of Tableau server users; let’s say thousands publish a huge number of data sources. You will agree that it will look disorganized when you don’t know who published a particular data source or which data source a user published.  It is important to know that on the server, there is always a unique identifier(id) for each data source. Also, there is an owner identifier (owner_id) of the data source. The owner identifier(owner_id) indicates the user that publishes on the server which also corresponds to the user identifier(user_id).  I assume you have Python 3 installed if not you can download anaconda which comes with Python packages. In this tutorial, you will learn how to get Tableau server user details and data sources using Tableau Server Client. Let’s get in. Step 1: Connect to the Tableau Server using the Tableau Server Client(TSC) library. Input these lines of code which includes the username and password to login to the server. import tableauserverclient as TSC import pandas as pd tableau_auth = TSC.TableauAuth(‘Username’, ‘Password’) This line of code includes the connection link to the server and login. server = TSC.Server(‘http://3.227.165.186’) request_options = TSC.RequestOptions(pagesize=1000) with server.auth.sign_in(tableau_auth): Step 2: Get the list of all the users. Input this line of code to fetch the list of all the users on the Tableau server. Then input the user id and the user name of the Tableau server in a dictionary, a key value pair. all_users = list(TSC.Pager(server.users, request_options)) userinfo = [{user.id:user.name} for user in all_users] Step 3: Get the list of all the data sources. Now, we will get the list of all the data sources and all the unique data source identifiers on the Tableau server. Input these lines of code all_datasources = list(TSC.Pager(server.datasources, request_options)) datasource_ids = [datasource.id for datasource in all_datasources] Step 4: Use Pandas library to get the user id, the user name, the data source id, and the data source owner id in a data frame. Placing the user information into a Pandas Data Frame is a good way to interact with the list and perform other operations. (a)Input the code below as a template for getting user details into a Pandas Data Frame and sign out afterward. datasource_owner_id = [{datasource.name:datasource.owner_id} for datasource in all_datasources] print(‘The datasource name and the consecutive datasource owner_id for the default site is as follows:’,datasource_owner_id) server.auth.sign_out() The next part of the code will get the user id and the name of the server users. It will then turn it to columns of a data frame. Now, we can open a list for key1, key22, value1, and value22. Then append the dictionary values and keys to the list. Append key2 to key22 empty list and value2 to the value22 empty list for userdiction in userinfo: for key2, value2 in userdiction.items(): key22.append(key2) value22.append(value2) df = pd.DataFrame({“datasource_owner_id”:key22,”Name”:value22}) (b) Make a loop to go through the dictionary in the data source_owner_id. Get the key and the value of the Dictionary “data”  and append the key to the key1 empty list. Then, append the value to the value1 empty list. key1 = [] value1 = [] for data in data source_ owner_id: for key, value in data.items(): key1.append(key) value1.append(value) df2 = pd.DataFrame({“datasource_name”:key1,”datasource_owner_id”:value1}) Note that at this point there are two different data frames. The first one is the df and the other is the df2. The df contains the list of all ids of the users of the server and the name of all the users of the server.     Step 5: Map the users of the server to all the data sources that they published. The next line of code joins the two data frames and merges the two data sets with the datasource_owner_id column. result = pd.merge(df, df2[[‘datasource_name’,’datasource_owner_id’]], on=’datasource_owner_id’) Step 6: Finally, save the data frame to a CSV file. print(result) result.to_csv The result of the joined table will look like the image below.  If you found this blog post helpful, kindly share it on social platforms with the buttons below. Questions are welcomed in the comments section.

Amazon Virtual Private Cloud, shortly known as Amazon VC or simply VPC, is a virtual network dedicated to your AWS account, that enables you to launch AWS resources to a virtual network you defined. It is the networking layer of your Amazon EC2. In this article, I will be sharing a walkthrough on how to carry out VPC peering in Amazon Web Services (AWS). A VPC peering connection is a one to one relationship between two VPCs. It is a networking connection between two VPCs that allows you to route traffic between them privately. You can create a peering connection between two of your VPCs, or with a VPC of another AWS account. For this article we will be peering an RDS database and an Amazon workspace, each having different VPCs. Here is what you’ll need: 1)A running RDS database on AWS. I am using Microsoft SQL. 2) Amazon workspace. STEPS 1. Log in to AWS console (https://console.aws.amazon.com/console/home) 2. Select “RDS” from the Database menu to launch it. 3. Select the database you want to peer with the AWS workspace by clicking on it. 4. From the page displayed afterward, Copy the VPC security group to your clipboard. We now have the VPC of the RDS database. The next thing to do is to get the VPC of the Amazon workspace. 5. Search for “workspace” using the search tab on your console’s interface. 6. Select the AWS workspace whose VPC you want to peer. You can reach out to your AWS admin to get the VPC of the Workspace. The next thing to do is to carry out the VPC peering in AWS. Search for VPC and click on it. 7. Click on Your VPC, as indicated by the yellow arrow in the image above, to see the VPCs running on your AWS console.   8. to establish peering, click on Peering Connections as indicated by the green arrow in the image above. 9. Select “Create Peering” and fill in the parameters as shown in the image below:   Peering Connection name (indicated with the blue arrow): Put your desire peering name/tag VPC (Requester) (indicated with the orange arrow):  Input the VPC of the AWS cloud service that wants to connect to another. In this scenario, the VPC of the RDS database. Select another VPC to peer with (indicated with the green arrow): Select the account and region where the other VPC is. In this scenario, we will choose “My account” and “This region”. This is because the two AWS services (RDS and workspace) were created with my account and are in the same region. Choose the second option if otherwise. VPC (Accepter) (indicated with the yellow arrow): The VPC of the AWS cloud service that wants to accept the connection. In this scenario, the VPC of the AWS workspace. 10. Select “Create Peering Connection” to establish a VPC peering connection. After the peering connection has been established, you should something like the picture below: VPC peering in Amazon Web Services has quite a few limitations and restrictions. You can check them out here.