SQL

KnolSnow: Load continuous data into Snowflake using Snowpipe

Reading Time: 5 minutes In this blog, we will discuss loading streaming data into Snowflake table using Snowpipe. But before that, if you haven’t read the previous part of this blog i.e., Loading Bulk Data into Snowflake then I would suggest you go through it. As now we have been set so let’s get started and see what Snowpipe is all about. Introduction Snowpipe is a mechanism provided by Continue Reading

Import multiple CSV files into the Postgres through Java/Scala code.

Reading Time: 2 minutes It’s pretty simple to ingest data in the Postgres using the insert query, but in the big data world, we have a lot of data that we can not insert using insert queries. We get the data in CSV files that we want to import directly to the Postgres. It will take a lot of effort and time if we will try to import these Continue Reading

KnolSnow: Loading Data Into Snowflake

Reading Time: 5 minutes This blog pertains to Loading Data into Snowflake, and I will explain you about the various step involved in this process. So let’s get started. Before moving ahead, you can visit the blog on understanding the basic of Snowflake Data Warehouse in case you want to refresh your concepts. Now let’s talk about the actual topic for which you have click on this blog. To Continue Reading

Apache Spark: Delta Lake as a Solution – Part I

Reading Time: 3 minutes Today, everyone is talking about Delta Lake. Why? Ever tried to find the answer to this question? Yes or No doesn’t matter, don’t worry here in Part1 we will be discussing the same & also will be targetting the following questions: What are the features missing from Apache Spark? What kind of issues it causes in executing Data Lake? Answering the above questions will definitely Continue Reading

Apache Spark: Handle Corrupt/Bad Records

Reading Time: 3 minutes Most of the time writing ETL jobs becomes very expensive when it comes to handling corrupt records. And in such cases, ETL pipelines need a good solution to handle corrupted records. Because, larger the ETL pipeline is, the more complex it becomes to handle such bad records in between. Corrupt data includes: Missing information Incomplete information Schema mismatch Differing formats or data types Apache Spark: Continue Reading

Parsing database Query with Apache Calcite

Reading Time: 3 minutes Hey there, as a technical person sometimes we have to write the query of database and that looks good but we don’t know the query we wrote was syntactically correct or not. So in this blog, we parse the database query and test it using a test case with the help of Apache Calcite. So not wasting any time lets discuss about Apache Calcite and Continue Reading

Database Normalization :: Part 2

Reading Time: 6 minutes Introduction Normalization helps one attain a good database design and thereby ensures continues efficiency of the database. Normalization, which is a process for assigning attributes to entities, offers the following advantages: There are 7 types of Normal forms: In my previous blog, Database Normalization :: Part 1 I’ve discussed about first four.In this blog, we will be looking into 4NF, 5NF and DKNF. Fourth Normal Continue Reading

Database Normalization :: Part 1

Reading Time: 6 minutes Introduction Normalization helps one attain a good database design and thereby ensures continues efficiency of the database. Normalization, which is a process for assigning attributes to entities, offers the following advantages: There are 7 types of Normal forms: In this blog, we will be looking into the first four only, rest I’ll be covering in Part 2 of Database Normalization. First Normal Form (1NF) :- Continue Reading

Amazon EMR

Reading Time: 3 minutes Businesses worldwide are discovering the power of new big data processing and analytics frameworks like Apache Hadoop and Apache Spark, but they are also discovering some of the challenges of operating these technologies in on-premises data lake environments. They may also have concerns about the future of their current distribution vendor. Common problems of on-premises big data environments include a lack of agility, excessive costs, Continue Reading

Apache Spark: Tricks to Increase Job Performance

Reading Time: 2 minutes Apache Spark is quickly adopting the Real-world and most of the companies like Uber are using it in their production. Spark is gaining its popularity in the market as it also provides you with the feature of developing Streaming Applications and doing Machine Learning, which helps companies get better results in their production along with proper analysis using Spark. Although companies are using Spark in Continue Reading

Apache Spark: Read Data from S3 Bucket

Reading Time: < 1 minute Amazon S3 Accessing S3 Bucket through Spark Edit spark-default.conf file You need to add below 3 lines consists of your S3 access key, secret key & file system